PeSA 2.0: A software tool for peptide specificity analysis implementing positive and negative motifs and motif-based peptide scoring

Topcu, Emine; Ridgeway, Nashira H.; Biggar, Kyle K.

doi:10.1016/j.compbiolchem.2022.107753

Cited by 2 publications

(4 citation statements)

References 27 publications

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“…The N6-acetyl-lysine predictive function of MusiteDeep was employed to generally identify sites of lysine acetylation, much like MethylSight for SET8. MusiteDeep outperformed other representative ML and deep learning algorithms for N6-acetyl-lysine prediction 29 .…”

Section: Machine Learning -Ensemble Learningmentioning

confidence: 95%

“…Densitometry results processed by PeSA2.0 yielded the following motif: 1) 29 . A search of the known methyllysine proteome (Supplementary Table 2) was performed with the scoring matrix, and a normalized score cutoff of 0.5, relative to unmodi ed H4K20 peptide (assigned a score of 1), yielded 346 hits (Supplementary Table 3).…”

Section: Set8 Expression and Conventional Substrate Prediction With P...mentioning

confidence: 99%

“…By chemically synthesizing a representative PTM proteome using peptide arrays, then subjecting them to in vitro enzymatic activity, we can characterize enzymatic PTM activity in a facile way. With the development of a machine learning model, augmented by generalised PTM-speci c prediction 11,29 , we created ML-hybrid ensemble models unique to each enzyme that demonstrates that signi cantly enhanced predictive accuracy in cell models (Fig. 1).…”

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: Machine Learning -Ensemble Learningmentioning

confidence: 95%

Section: Set8 Expression and Conventional Substrate Prediction With P...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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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“…Designing a proper peptide library for kinome analysis is one of the most crucial aspects in ensuring its accuracy. The proper peptide can be screened via one-bead-one-compound [ 31 ], phage display [ 32 ], or PeSA [ 33 ]. The PeSA is a software tool, which uses the peptide array data for sequence analysis.…”

Section: Technologies For Kinome Analysismentioning

confidence: 99%

Mapping the Protein Kinome: Current Strategy and Future Direction

Hou

Liu²

2023

Cells

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The kinome includes over 500 different protein kinases, which form an integrated kinase network that regulates cellular phosphorylation signals. The kinome plays a central role in almost every cellular process and has strong linkages with many diseases. Thus, the evaluation of the cellular kinome in the physiological environment is essential to understand biological processes, disease development, and to target therapy. Currently, a number of strategies for kinome analysis have been developed, which are based on monitoring the phosphorylation of kinases or substrates. They have enabled researchers to tackle increasingly complex biological problems and pathological processes, and have promoted the development of kinase inhibitors. Additionally, with the increasing interest in how kinases participate in biological processes at spatial scales, it has become urgent to develop tools to estimate spatial kinome activity. With multidisciplinary efforts, a growing number of novel approaches have the potential to be applied to spatial kinome analysis. In this paper, we review the widely used methods used for kinome analysis and the challenges encountered in their applications. Meanwhile, potential approaches that may be of benefit to spatial kinome study are explored.

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PeSA 2.0: A software tool for peptide specificity analysis implementing positive and negative motifs and motif-based peptide scoring

Cited by 2 publications

References 27 publications

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

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

Mapping the Protein Kinome: Current Strategy and Future Direction

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