Processive cleavage of substrate at individual proteolytic active sites of the Lon protease complex

S, Li; Hsieh, Kan-Yen; Kuo, C.L.; Su, Shih-Chieh; Huang, Kai‐Fa; Zhang, Kaiming; Chang, Chin-Fu

doi:10.1126/sciadv.abj9537

Cited by 12 publications

(8 citation statements)

References 53 publications

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“…Rigid body fitting of three N-terminal homology domains indicated that the extended substrate protein contacted H391 and Y394 of a conserved HRKY linker helix motif. In bacterial LonPs from Thermus thermophilus (Coscia and Lowe, 2021) and Meiothermus taiwanesis (Li et al, 2021), the arrangement of the odd-numbered N domains is like in LonP1. In the absence of density in the scattering potential maps, even-numbered N domains of these bacterial LonPs were modeled under the assumption that their long linker a-helices would be straight.…”

Section: Overall Architecturementioning

confidence: 99%

Catalytic cycling of human mitochondrial Lon protease

et al. 2022

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Section: Overall Architecturementioning

confidence: 99%

Catalytic cycling of human mitochondrial Lon protease

et al. 2022

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“…Furthermore, while experimental evidence is regarded as the ground truth when creating prediction models, the PDB has known issues such as redundancy 44 or enzyme-inhibitor complexes marked as enzyme-substrate complexes [45][46][47] . For serine endopeptidase La (EC: 3.4.21.53), none of the eight test PDBs are correctly predicted.…”

Section: Datasets Usedmentioning

confidence: 99%

“…For serine endopeptidase La (EC: 3.4.21.53), none of the eight test PDBs are correctly predicted. Notably, one is described to have no activity (4fwh), three are characterized in complex with inhibitors (4fw9, 4fwd, 4fwg), and the other four are from a mutation study where they mutated the catalytic dyad residues (7ev4, 7euy, 7ev6, 7eux) 46,47 . Yet, these structures are all marked as having enzymatic activity with EC classification 3.4.21.53 in the PDB.…”

Section: Datasets Usedmentioning

confidence: 99%

TopEC: Improved classification of enzyme function by a localized 3D protein descriptor and 3D Graph Neural Networks

van der Weg,

Merdivan,

Piraud

et al. 2024

Preprint

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Accurately annotating molecular function to enzymes remains challenging. Computational methods can aid in this and allow for high-throughput annotation. Tools available for inferring enzyme function from general sequence, fold, or evolutionary information are generally successful. However, they can lead to misclassification if for certain sequences a deviation in local structural features influences the function. Here, we present TopEC, a 3D graph neural network based on a localized 3D descriptor to learn chemical reactions of enzymes from (predicted) enzyme structures and predict Enzyme Commission (EC) classes. Using the message passing frameworks from SchNet and DimeNet++, we include distance and angle information to improve the predictive performance compared to regular 2D graph neural networks. We obtained significantly improved EC classification prediction (F-score: 0.72) without fold bias at residue and atomic resolutions and trained networks that can classify both experimental and computationally generated enzyme structures for a vast functional space (> 800 ECs). Our model is robust to uncertainties in binding site locations and similar functions in distinct binding sites. By investigating the importance of each graph node to the predictive performance, we see that TopEC networks learn from an interplay between biochemical features and local shape-dependent features.

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“…Finally, another omnipresent energy‐dissipating protease, LonA, uses ATP but also polyphosphate as its energy source (Ropelewska et al, 2020 ). It degrades protein substrates in a processive fashion (Li, Hsieh, et al, 2021 ). As stated previously, it uses SmiA as an adaptator for SwrA degradation (Hughes et al, 2018 ; Olney et al, 2022 ).…”

Section: The Proteostasis Networkmentioning

confidence: 99%

A model industrial workhorse: Bacillus subtilis strain 168 and its genome after a quarter of a century

Bremer

Calteau

Danchin

et al. 2023

Microbial Biotechnology

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The vast majority of genomic sequences are automatically annotated using various software programs. The accuracy of these annotations depends heavily on the very few manual annotation efforts that combine verified experimental data with genomic sequences from model organisms. Here, we summarize the updated functional annotation of Bacillus subtilis strain 168, a quarter century after its genome sequence was first made public. Since the last such effort 5 years ago, 1168 genetic functions have been updated, allowing the construction of a new metabolic model of this organism of environmental and industrial interest. The emphasis in this review is on new metabolic insights, the role of metals in metabolism and macromolecule biosynthesis, functions involved in biofilm formation, features controlling cell growth, and finally, protein agents that allow class discrimination, thus allowing maintenance management, and accuracy of all cell processes. New 'genomic objects' and an extensive updated literature review have been included for the sequence, now available at the International Nucleotide Sequence Database Collaboration (INSDC: AccNum AL009126.4).

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Processive cleavage of substrate at individual proteolytic active sites of the Lon protease complex

Abstract: One-way translocation and processive cleavage of substrate polypeptide occur in each of the Lon proteolytic active sites.

Cited by 12 publications

References 53 publications

Catalytic cycling of human mitochondrial Lon protease

Catalytic cycling of human mitochondrial Lon protease

TopEC: Improved classification of enzyme function by a localized 3D protein descriptor and 3D Graph Neural Networks

A model industrial workhorse: Bacillus subtilis strain 168 and its genome after a quarter of a century

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