AI-driven pan-proteome analyses reveal insights into the biohydrometallurgical properties of <i>Acidithiobacillia</i>.

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作者： Liangzhi,Li ^[1] ; Lei,Zhou ^[2] ; Chengying,Jiang ^[3] ; Zhenghua,Liu ^[4] ; Delong,Meng ^[5] ; Feng,Luo ^[1] ; Qiang,He ^[2] ; Huaqun,Yin ^[1]

作者单位： School of Minerals Processing and Bioengineering, Central South University, Changsha, China. ^[1] Key Laboratory of Biometallurgy of Ministry of Education, Central South University, Changsha, China. ^[2] Beijing Research Institute of Chemical Engineering and Metallurgy, Beijing, China. ^[3] State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China. ^[4] University of Chinese Academy of Sciences, Beijing, China. ^[5] School of Computing, Clemson University, Clemson, SC, United States. ^[6] Department of Civil and Environmental Engineering, University of Tennessee, Knoxville, Knoxville, TN, United States. ^[7]

关键词 Acidithiobacillia biohydrometallurgy gene co-occurrence network analysis protein structure prediction proteome

DOI 10.3389/fmicb.2023.1243987

PMID 37744906

发布时间 2023-09-26

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Frontiers in microbiology

2023年14卷

1243987页

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AI-driven pan-proteome analyses reveal insights into the biohydrometallurgical properties of <i>Acidithiobacillia</i>.

Frontiers in microbiology

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Frontiers in microbiology

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AI-driven pan-proteome analyses reveal insights into the biohydrometallurgical properties of <i>Acidithiobacillia</i>.

Frontiers in microbiology

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Frontiers in microbiology

相关期刊

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