Abstract
Domain–motif interactions (DMIs) represent transient bonds formed when a Short Linear Motif (SLiM) engages a globular domain via a compact contact interface. Understanding the mechanics of DMIs is critical for maintaining diverse regulatory processes and deciphering how various viruses hijack host cellular machinery. However, identifying DMIs through traditional in vitro and in vivo experiments is challenging due to their degenerate nature and small contact areas. Predictions often carry a high rate of false positives, necessitating rigorous in-silico validation before embarking on experimental work. This study assessed the binding energy changes in predicted SLiM instances through in-silico peptide exchange experiment, elucidating how they interact with known 3D DMI complexes. We identified a subset of potential mimicry candidates that exhibited effective binding affinities with native DMI structures, suggesting their potential to be true mimicry candidates. The identified viral SLiMs can be potential targets in developing therapeutics, opening new opportunities for innovative treatments that can be finely tuned to address the complex molecular underpinnings of various diseases. To gain a comprehensive understanding of identified DMIs, it is imperative to conduct further validation through experimental approaches.
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This article contains excerpts from Idrees' thesis published in 2020 (Idrees 2020).
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Acknowledgements
The authors would like to acknowledge the University of New South Wales, Sydney, and Dr. Richard J. Edwards.
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This work was supported by the University of New South Wales through a University International Postgraduate Award to Sobia Idrees. The authors received no funding support for publication of this article.
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SI performed the experiments, analysed the data, contributed analysis tools, prepared figures and/or tables, authored or reviewed drafts of the paper, and approved the final draft. KRP authored or reviewed drafts of the paper and approved the final draft.
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Communicated by Yusuf Akhter.
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Idrees, S., Paudel, K.R. Bioinformatics prediction and screening of viral mimicry candidates through integrating known and predicted DMI data. Arch Microbiol 206, 30 (2024). https://doi.org/10.1007/s00203-023-03764-w
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DOI: https://doi.org/10.1007/s00203-023-03764-w