Protocol for CAROM: A machine learning tool to predict post-translational regulation from metabolic signatures

Smith, K. E.; Rhoads, Nicole; Chandrasekaran, Sriram

doi:10.1016/j.xpro.2022.101799

Cited by 2 publications

(2 citation statements)

References 21 publications

(30 reference statements)

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“…The innovative fusion of in vitro experiments with in silico predictions, exempli ed by methodologies such as the Bayesian framework employed to characterize the substrates of protein-tyrosine phosphatase, PTP1B, using protein-protein interaction prediction, offer a promising avenue for substrate prediction 18 . However, such methods are limited by their reliance on databases that can be poorly representative of the enzyme of interest or of uncertain quality 19 . Often machine learning (ML)-based PTM prediction methods that are enzyme-speci c require details about structure or the metabolic networks of the enzyme 19,20 .…”

Section: Introductionmentioning

confidence: 99%

“…However, such methods are limited by their reliance on databases that can be poorly representative of the enzyme of interest or of uncertain quality 19 . Often machine learning (ML)-based PTM prediction methods that are enzyme-speci c require details about structure or the metabolic networks of the enzyme 19,20 . In this study, we transcend traditional techniques by adopting a ML-hybrid ensemble approach to enzyme-substrate identi cation that is generalizable across diverse enzyme classes.…”

Section: Introductionmentioning

confidence: 99%

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Machine learning-based exploration of enzyme-substrate networks: SET8-mediated methyllysine and its changing impact within cancer proteomes

Biggar,

Ridgeway,

Chopra

et al. 2024

Preprint

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The exploration of post-translational modifications (PTMs) within the proteome is pivotal for advancing disease and cancer therapeutics. However, identifying genuine PTM sites amid numerous candidates is challenging. Integrating machine learning (ML) models with high-throughput in vitro peptide synthesis has introduced an ML-hybrid search methodology, enhancing enzyme-substrate selection prediction. In this study we have developed a ML-hybrid search methodology to better predict enzyme-substrate selection. This model achieved a 37.4% experimentally validated precision, unveiling 885 SET8 candidate methylation sites in the human proteome—marking a 19-fold accuracy increase over traditional in vitro methods. Mass spectrometry analysis confirmed the methylation status of several sites, responding positively to SET8 overexpression in mammalian cells. This approach to substrate discovery has also shed light on the changing SET8-regulated substrate network in breast cancer, revealing a predicted gain (376) and loss (62) of substrates due to missense mutations. By unraveling enzyme selection features, this approach offers transformative potential, revolutionizing enzyme-substrate discovery across diverse PTMs while capturing crucial biochemical substrate properties.

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Section: Introductionmentioning

confidence: 99%