CNNArginineMe: A CNN structure for training models for predicting arginine methylation sites based on the One-Hot encoding of peptide sequence

Zhao, Jiaojiao; Jiang, Haoqiang; Zou, Guoyang; Lin, Qian; Wang, Qiang; Liu, Jia; Ma, Leina

doi:10.3389/fgene.2022.1036862

Cited by 7 publications

(3 citation statements)

References 60 publications

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“…Additionally, while both of these computational prediction programs used stringent datasets, they may have assigned a disproportionate weight to positive flanking residues that are known to affect substrate binding and catalysis by PRMT active site . In preparation for this manuscript, we have discovered that a new prediction program that addresses overfitting by early stopping is now available …”

Section: Discussionmentioning

confidence: 99%

“… 71 In preparation for this manuscript, we have discovered that a new prediction program that addresses overfitting by early stopping is now available. 72 …”

Section: Discussionmentioning

confidence: 99%

“…71 In preparation for this manuscript, we have discovered that a new prediction program that addresses overfitting by early stopping is now available. 72 The exact role of temperature-dependent arginine methylation of PGC-1α by PRMT7 remains unclear. As an IDP, PGC-1α has many protein partners, which affect its function as a master regulator.…”

Section: ■ Conclusionmentioning

confidence: 99%

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Arginine Methylation of the PGC-1α C-Terminus Is Temperature-Dependent

Mendoza

Lubrino

et al. 2022

Biochemistry

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We set out to determine whether the C-terminus (amino acids 481–798) of peroxisome proliferator-activated receptor gamma coactivator-1 alpha (PGC-1α, UniProt Q9UBK2), a regulatory metabolic protein involved in mitochondrial biogenesis, and respiration, is an arginine methyltransferase substrate. Arginine methylation by protein arginine methyltransferases (PRMTs) alters protein function and thus contributes to various cellular processes. In addition to confirming methylation of the C-terminus by PRMT1 as described in the literature, we have identified methylation by another member of the PRMT family, PRMT7. We performed in vitro methylation reactions using recombinant mammalian PRMT7 and PRMT1 at 37, 30, 21, 18, and 4 °C. Various fragments of PGC-1α corresponding to the C-terminus were used as substrates, and the methylation reactions were analyzed by fluorography and mass spectrometry to determine the extent of methylation throughout the substrates, the location of the methylated PGC-1α arginine residues, and finally, whether temperature affects the deposition of methyl groups. We also employed two prediction programs, PRmePRed and MePred-RF, to search for putative methyltransferase sites. Methylation reactions show that arginine residues R548 and R753 in PGC-1α are methylated at or below 30 °C by PRMT7, while methylation by PRMT1 was detected at these same residues at 30 °C. Computational approaches yielded additional putative methylarginine sites, indicating that since PGC-1α is an intrinsically disordered protein, additional methylated arginine residues have yet to be experimentally verified. We conclude that temperature affects the extent of arginine methylation, with more methylation by PRMT7 occurring below physiological temperature, uncovering an additional control point for PGC-1α.

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Section: Discussionmentioning

confidence: 99%

“… 71 In preparation for this manuscript, we have discovered that a new prediction program that addresses overfitting by early stopping is now available. 72 …”

Section: Discussionmentioning

confidence: 99%

Section: ■ Conclusionmentioning

confidence: 99%

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Arginine Methylation of the PGC-1α C-Terminus Is Temperature-Dependent

Mendoza

Lubrino

et al. 2022

Biochemistry

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Advances in Prediction of Posttranslational Modification Sites Known to Localize in Protein Supersecondary Structures

Pratyush,

2024

Methods in Molecular Biology

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Post-translational modification prediction via prompt-based fine-tuning of a GPT-2 model

Shrestha,

Kandel,

Tayara

et al. 2024

Nat Commun

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Post-translational modifications (PTMs) are pivotal in modulating protein functions and influencing cellular processes like signaling, localization, and degradation. The complexity of these biological interactions necessitates efficient predictive methodologies. In this work, we introduce PTMGPT2, an interpretable protein language model that utilizes prompt-based fine-tuning to improve its accuracy in precisely predicting PTMs. Drawing inspiration from recent advancements in GPT-based architectures, PTMGPT2 adopts unsupervised learning to identify PTMs. It utilizes a custom prompt to guide the model through the subtle linguistic patterns encoded in amino acid sequences, generating tokens indicative of PTM sites. To provide interpretability, we visualize attention profiles from the model’s final decoder layer to elucidate sequence motifs essential for molecular recognition and analyze the effects of mutations at or near PTM sites to offer deeper insights into protein functionality. Comparative assessments reveal that PTMGPT2 outperforms existing methods across 19 PTM types, underscoring its potential in identifying disease associations and drug targets.

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CNNArginineMe: A CNN structure for training models for predicting arginine methylation sites based on the One-Hot encoding of peptide sequence

Cited by 7 publications

References 60 publications

Arginine Methylation of the PGC-1α C-Terminus Is Temperature-Dependent

Arginine Methylation of the PGC-1α C-Terminus Is Temperature-Dependent

Advances in Prediction of Posttranslational Modification Sites Known to Localize in Protein Supersecondary Structures

Post-translational modification prediction via prompt-based fine-tuning of a GPT-2 model

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