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<p><i>Burkholderia pseudomallei</i> (<i>BP</i>) infections claims tens of thousands of lives worldwide every year. The bacterium's distinctive characteristics include antibiotic resistance, virulence and ability to survive in stressful environments. The <i>B. pseudomallei</i> genome sequencing and annotation reveal that about 25% of the genes encode hypothetical proteins (HPs). As such, characterising the HPs could shed light on the mechanisms that contribute to the above characteristics. Over the last decade, genome sequencing and annotation technologies have advanced drastically. Furthermore, artificial intelligence programs such as AlphaFold2 (AF2), RoseTTAFold2 (RF2), which can predict 3D protein structures with high accuracy, are also available. Taking advantage of the available tools, this study aimed to re-annotate HPs that are encoded within the <i>BP</i> genome. To achieve this, we retrieved 1869 HPs from the <i>Burkholderia</i> Genome Database, then cross-referenced with UniProt. After filtering, 419 remain hypothetical. These were analysed using BLASTp for sequence homologs and antibiotic resistance proteins, followed by 3D structure prediction using AF2 and RF2, and structural homolog search using Foldseek. This study successfully annotated 209 HPs with only 210 proteins (3.7% of <i>BP</i> coding sequences) still classified as ‘hypothetical’. The functions of the predicted HPs were further analysed using structure comparison and active site analysis. The annotated protein list includes fifteen antibiotic resistance proteins, five haem oxygenase-like fold proteins involved in biofilm formation, host pathogenesis, and antibacterial activity, along with five essential proteins. These proteins represent promising drug targets for developing new antibiotics against melioidosis. Nonetheless, experimental validation will be necessary to characterize the predicted protein functions.</p> Graphical Abstract <p></p>

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Unveiling Putative Functions of Burkholderia pseudomallei K96243 Hypothetical Proteins Via High-Throughput Characterization of Structural Similarities

  • 期刊论文

  • 发表日期：

    2025年12月12日

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  • Syed Abuthakir Mohamed Husain,
  • Su Datt Lam,
  • Mohd Firdaus-Raih,
  • Sheila Nathan,
  • Nor Azlan Nor Muhammad,
  • Chyan Leong Ng

摘要

Burkholderia pseudomallei (BP) infections claims tens of thousands of lives worldwide every year. The bacterium's distinctive characteristics include antibiotic resistance, virulence and ability to survive in stressful environments. The B. pseudomallei genome sequencing and annotation reveal that about 25% of the genes encode hypothetical proteins (HPs). As such, characterising the HPs could shed light on the mechanisms that contribute to the above characteristics. Over the last decade, genome sequencing and annotation technologies have advanced drastically. Furthermore, artificial intelligence programs such as AlphaFold2 (AF2), RoseTTAFold2 (RF2), which can predict 3D protein structures with high accuracy, are also available. Taking advantage of the available tools, this study aimed to re-annotate HPs that are encoded within the BP genome. To achieve this, we retrieved 1869 HPs from the Burkholderia Genome Database, then cross-referenced with UniProt. After filtering, 419 remain hypothetical. These were analysed using BLASTp for sequence homologs and antibiotic resistance proteins, followed by 3D structure prediction using AF2 and RF2, and structural homolog search using Foldseek. This study successfully annotated 209 HPs with only 210 proteins (3.7% of BP coding sequences) still classified as ‘hypothetical’. The functions of the predicted HPs were further analysed using structure comparison and active site analysis. The annotated protein list includes fifteen antibiotic resistance proteins, five haem oxygenase-like fold proteins involved in biofilm formation, host pathogenesis, and antibacterial activity, along with five essential proteins. These proteins represent promising drug targets for developing new antibiotics against melioidosis. Nonetheless, experimental validation will be necessary to characterize the predicted protein functions.

Graphical Abstract