Protein structure prediction with in-cell photo-crosslinking mass spectrometry and deep learning

Stahl, Kolja; Graziadei, Andrea; Dau, Therese; Brock, Oliver; Rappsilber, Juri

doi:10.1038/s41587-023-01704-z

Cited by 74 publications

(49 citation statements)

References 49 publications

(52 reference statements)

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“…Recently, research efforts have been emerging on combining the power of machine deep learning and data from molecular dynamics simulations as a method of studying allosteric effects 46 .…”

Section: Discussionmentioning

confidence: 99%

Repression of ferroptotic cell death by mitochondrial calcium signaling

Wen

Chen

Bao

et al. 2023

Preprint

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The uptake of Ca2+ into and extrusion of calcium from the mitochondrial matrix, regulated by the mitochondrial Ca2+ uniporter (MCU), is a fundamental biological process that has crucial impacts on cellular metabolism, signaling, growth and survival. Herein, we report that the embryonic lethality of Mcu-deficient mice is fully rescued by orally supplementing ferroptosis inhibitor lipophilic antioxidant vitamin E and ubiquinol. Mechanistically, we found MCU promotes acetyl-CoA-mediated GPX4 acetylation at K90 residue, and K90R mutation impaired the GPX4 enzymatic activity, a step that is crucial for ferroptosis. Structural analysis supports the possibility that GPX4 K90R mutation alters the conformational state of the molecule, resulting in disruption of a salt bridge formation with D23, which was confirmed by mutagenesis studies. Finally, we report that deletion of MCU in cancer cells caused a marked reduction in tumor growth in multiple cancer models. In summary, our study provides a first direct link between mitochondrial calcium level and sustained GPX4 enzymatic activity to regulate ferroptosis, which consequently protects cancer cells from ferroptosis.

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“…Recently, research efforts have been emerging on combining the power of machine deep learning and data from molecular dynamics simulations as a method of studying allosteric effects 46 .…”

Section: Discussionmentioning

confidence: 99%

Repression of ferroptotic cell death by mitochondrial calcium signaling

Wen

Chen

Bao

et al. 2023

Preprint

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“…Next to using the NMR data in a sequential manner to validate or post-process computationally predicted models, certain NMR data could also be directly incorporated in the network architectures of RoseTTAFold or AlphaFold2. Recently, Stahl et al developed AlphaLink [157], a modified version of AlphaFold2 that incorporates MS cross-linking (XL) data into the AlphaFold2 network architecture. The XL contact restraints complement and refine the evolutionary-based contact information, and, in return, the co-evolutionary contacts suppress noisy XL data.…”

Section: Augmentation Of Deep Learning Methods With Nmr Datamentioning

confidence: 99%

“…The XL contact restraints complement and refine the evolutionary-based contact information, and, in return, the co-evolutionary contacts suppress noisy XL data. AlphaLink offers improved performance compared to AlphaFold2 in cases of challenging targets such as proteins with shallow multiple sequence alignments (MSAs) or multiple conformational states [157]. The authors note that their approach is also applicable to other types of experimental distance information (e.g., NOEs).…”

Section: Augmentation Of Deep Learning Methods With Nmr Datamentioning

confidence: 99%

Recent Advances in NMR Protein Structure Prediction with ROSETTA

Leman

Künze

2023

IJMS

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Nuclear magnetic resonance (NMR) spectroscopy is a powerful method for studying the structure and dynamics of proteins in their native state. For high-resolution NMR structure determination, the collection of a rich restraint dataset is necessary. This can be difficult to achieve for proteins with high molecular weight or a complex architecture. Computational modeling techniques can complement sparse NMR datasets (<1 restraint per residue) with additional structural information to elucidate protein structures in these difficult cases. The Rosetta software for protein structure modeling and design is used by structural biologists for structure determination tasks in which limited experimental data is available. This review gives an overview of the computational protocols available in the Rosetta framework for modeling protein structures from NMR data. We explain the computational algorithms used for the integration of different NMR data types in Rosetta. We also highlight new developments, including modeling tools for data from paramagnetic NMR and hydrogen–deuterium exchange, as well as chemical shifts in CS-Rosetta. Furthermore, strategies are discussed to complement and improve structure predictions made by the current state-of-the-art AlphaFold2 program using NMR-guided Rosetta modeling.

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“…RF showed improved accuracy toward protein–protein complex prediction as compared to AF2 and AF2 multimer. , OpenFold2 was also developed to replicate the AF2 algorithm and make it accessible to the structural biology community . AlphaLink was also introduced to incorporate experimental distance restraint information, thus generating a modified version of the AF2 network architecture …”

Section: Application Of Af2 and Other Deep Learning Techniques For Pr...mentioning

confidence: 99%

AlphaFold2 and Deep Learning for Elucidating Enzyme Conformational Flexibility and Its Application for Design

Casadevall

Duran

Osuna

2023

JACS Au

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The recent success of AlphaFold2 (AF2) and other deep learning (DL) tools in accurately predicting the folded three-dimensional (3D) structure of proteins and enzymes has revolutionized the structural biology and protein design fields. The 3D structure indeed reveals key information on the arrangement of the catalytic machinery of enzymes and which structural elements gate the active site pocket. However, comprehending enzymatic activity requires a detailed knowledge of the chemical steps involved along the catalytic cycle and the exploration of the multiple thermally accessible conformations that enzymes adopt when in solution. In this Perspective, some of the recent studies showing the potential of AF2 in elucidating the conformational landscape of enzymes are provided. Selected examples of the key developments of AF2-based and DL methods for protein design are discussed, as well as a few enzyme design cases. These studies show the potential of AF2 and DL for allowing the routine computational design of efficient enzymes.

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Protein structure prediction with in-cell photo-crosslinking mass spectrometry and deep learning

Cited by 74 publications

References 49 publications

Repression of ferroptotic cell death by mitochondrial calcium signaling

Repression of ferroptotic cell death by mitochondrial calcium signaling

Recent Advances in NMR Protein Structure Prediction with ROSETTA

AlphaFold2 and Deep Learning for Elucidating Enzyme Conformational Flexibility and Its Application for Design

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