The Stability Landscape of de novo TIM Barrels Explored by a Modular Design Approach

Romero‐Romero, Sergio; Costas, Miguel; Manzano, Daniel-Adriano Silva; Kordes, Sina; Rojas-Ortega, Eréndira; Tapia, Cinthya; Guerra, Yasel; Shanmugaratnam, S.; Rodrı́guez-Romero, Adela; Baker, David; Höcker, Birte; Fernández‐Velasco, D. Alejandro

doi:10.1016/j.jmb.2021.167153

Cited by 20 publications

(23 citation statements)

References 90 publications

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“…Lastly, we compared the energetics of the CAV1 barrel with two other parallel β-barrels to assess the effects of barrel size on their stability ( Figure 7, Table 1 ). Most parallel β-barrels in nature belong to the TIM-barrel family whose members consist of eight β-strands that fold typically into a barrel with C4 symmetry [21] ( Figure 7 ). Another example was recruited from a soluble phage protein ( Bacillus cereus ) whose C-termini folds into a central 9-meric parallel β-barrel (2AO9) ( Figure 7 ).…”

Section: Resultsmentioning

confidence: 99%

Template-free prediction of a new monotopic membrane protein fold and oligomeric assembly by Alphafold2

Gulsevin

Han

Porta

et al. 2022

Preprint

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AlphaFold2 (AF2) has revolutionized the field of protein structural prediction. Here, we test its ability to predict the tertiary and quaternary structure of a previously undescribed scaffold with new folds and unusual architecture, the monotopic membrane protein caveolin-1 (CAV1). CAV1 assembles into a disc-shaped oligomer composed of 11 symmetrically arranged protomers, each assuming an identical new fold, and contains the largest parallel β-barrel known to exist in nature. Remarkably, AF2 predicts both the fold of the protomers and interfaces between them. It also assembles between 7 and 15 copies of CAV1 into disc-shaped complexes. However, the predicted multimers are energetically strained, especially the parallel β-barrel. These findings highlight the ability of AF2 to correctly predict new protein folds and oligomeric assemblies at a granular level while missing some elements of higher order complexes, thus positing a new direction for the continued development of deep learning protein structure prediction approaches.

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Section: Resultsmentioning

confidence: 99%

Template-free prediction of a new monotopic membrane protein fold and oligomeric assembly by Alphafold2

Gulsevin

Han

Porta

et al. 2022

Preprint

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“…4, we show structures 486 and 1060, two examples of such complex structures. In particular, 1068 shows a TIMbarrel fold, a topology which to date has met impressive success in de novo design [62][63][64] , but whose idealized structure has nevertheless proven challenging to extend via additional secondary elements and longer loops 65,66 .…”

Section: Protgpt2 Transcends the Boundaries Of The Current Protein Spacementioning

confidence: 99%

ProtGPT2 is a deep unsupervised language model for protein design

2022

Self Cite

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Protein design aims to build novel proteins customized for specific purposes, thereby holding the potential to tackle many environmental and biomedical problems. Recent progress in Transformer-based architectures has enabled the implementation of language models capable of generating text with human-like capabilities. Here, motivated by this success, we describe ProtGPT2, a language model trained on the protein space that generates de novo protein sequences following the principles of natural ones. The generated proteins display natural amino acid propensities, while disorder predictions indicate that 88% of ProtGPT2-generated proteins are globular, in line with natural sequences. Sensitive sequence searches in protein databases show that ProtGPT2 sequences are distantly related to natural ones, and similarity networks further demonstrate that ProtGPT2 is sampling unexplored regions of protein space. AlphaFold prediction of ProtGPT2-sequences yields well-folded non-idealized structures with embodiments and large loops and reveals topologies not captured in current structure databases. ProtGPT2 generates sequences in a matter of seconds and is freely available.

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“…In Figure 3, we show structures 486 and 1060, two examples of such complex structures. In particular, 1068 shows a TIM-barrel fold, a topology which to date has met impressive success in de novo design (39)(40)(41), but whose idealized structure has nevertheless proven challenging to extend via additional secondary elements and longer loops (42,43).…”

Section: Protgpt2 Transcends the Boundaries Of The Current Protein Spacementioning

confidence: 99%

A deep unsupervised language model for protein design

Ferruz

Schmidt

Höcker

2022

Preprint

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Protein design aims to build new proteins from scratch, thereby holding the potential to tackle many environmental and biomedical problems. Recent progress in the field of natural language processing (NLP) has enabled the implementation of ever-growing language models capable of understanding and generating text with human-like capabilities. Given the many similarities between human languages and protein sequences, the use of NLP models offers itself for predictive tasks in protein research. Motivated by the evident success of generative Transformer-based language models such as the GPT-x series, we developed ProtGPT2, a language model trained on protein space that generates de novo protein sequences that follow the principles of natural ones. In particular, the generated proteins display amino acid propensities which resemble natural proteins. Disorder and secondary structure prediction indicate that 88% of ProtGPT2-generated proteins are globular, in line with natural sequences. Sensitive sequence searches in protein databases show that ProtGPT2 sequences are distantly related to natural ones, and similarity networks further demonstrate that ProtGPT2 is sampling unexplored regions of protein space. AlphaFold prediction of ProtGPT2-sequences yielded well-folded non-idealized structures with embodiments as well as large loops and revealed new topologies not captured in current structure databases. ProtGPT2 has learned to speak the protein language. It has the potential to generate de novo proteins in a high throughput fashion in a matter of seconds. The model is easy-to-use and freely available.

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The Stability Landscape of de novo TIM Barrels Explored by a Modular Design Approach

Abstract: Graphical abstract

Cited by 20 publications

References 90 publications

Template-free prediction of a new monotopic membrane protein fold and oligomeric assembly by Alphafold2

Template-free prediction of a new monotopic membrane protein fold and oligomeric assembly by Alphafold2

ProtGPT2 is a deep unsupervised language model for protein design

A deep unsupervised language model for protein design

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