The stability landscape of<i>de novo</i>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:10.1101/2020.09.29.319103

Cited by 6 publications

(6 citation statements)

References 67 publications

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“…Sequence overlap between TIM-barrel subunits for model TIM-barrel designs (orange) and previously characterized sequences for the same scaffold (blue), including sTIM-11 (5bvl, S11) [40], DeNovoTIM15 (6wvs, D15) [44], and NovoTIMs (N6, N13, N14a, N14b) [45]. Specific overlaps shown for the region between ( A ) the helices and the barrel, ( B ) the inner part of the barrel, ( C ) exposed regions, and ( D ) all residues.…”

Section: Supplementary Textmentioning

confidence: 99%

“…( B ) Distribution of rotamer automorphic RMSDs (Å) between crystal structure and template backbone for core and solvent-exposed residues for (top) F2C and (bottom) F15C. ( C ) Sequence overlap between TIM-barrel subunits for model TIM-barrel designs (orange) and previously characterized sequences for the same scaffold (blue), including sTIM-11 (5bvl, S11) [40], DeNovoTIM15 ( 6wvs , D15) [44], and NovoTIMs (N6, N13, N14a, N14b) [45]. Edge thickness increases with overlap.…”

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confidence: 99%

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Protein Sequence Design with a Learned Potential

Anand

Eguchi

Mathews

et al. 2020

Preprint

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The primary challenge of fixed-backbone protein sequence design is to find a distribution of sequences that fold to the backbone of interest. In practice, state-of-the-art protocols often find viable but highly convergent solutions. In this study, we propose a novel method for fixed-backbone protein sequence design using a learned deep neural network potential. We train a convolutional neural network (CNN) to predict a distribution over amino acids at each residue position conditioned on the local structural environment around the residues. Our method for sequence design involves iteratively sampling from this conditional distribution. We demonstrate that this approach is able to produce feasible, novel designs with quality on par with the state-of-the-art, while achieving greater design diversity. In terms of generalizability, our method produces plausible and variable designs for a de novo TIM-barrel structure, showcasing its practical utility in design applications for which there are no known native structures.

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Section: Supplementary Textmentioning

confidence: 99%

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confidence: 99%

Protein Sequence Design with a Learned Potential

Anand

Eguchi

Mathews

et al. 2020

Preprint

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“…We further introduced a small change to the sTIM11 scaffold by removing two cysteine residues that were originally introduced to form a disulfide bridge to improve barrel closure 9 . However, this disulfide bond did not form, so we removed the two cysteines to generate a cysteine‐free variant named sTIM11noCys whose characteristics and crystal structure are reported in Romero‐Romero et al 12 …”

Section: Resultsmentioning

confidence: 99%

Extension of a de novo TIM barrel with a rationally designed secondary structure element

2021

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The ability to construct novel enzymes is a major aim in de novo protein design. A popular enzyme fold for design attempts is the TIM barrel. This fold is a common topology for enzymes and can harbor many diverse reactions. The recent de novo design of a four‐fold symmetric TIM barrel provides a well understood minimal scaffold for potential enzyme designs. Here we explore opportunities to extend and diversify this scaffold by adding a short de novo helix on top of the barrel. Due to the size of the protein, we developed a design pipeline based on computational ab initio folding that solves a less complex sub‐problem focused around the helix and its vicinity and adapt it to the entire protein. We provide biochemical characterization and a high‐resolution X‐ray structure for one variant and compare it to our design model. The successful extension of this robust TIM‐barrel scaffold opens opportunities to diversify it towards more pocket like arrangements and as such can be considered a building block for future design of binding or catalytic sites.

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“…Further analysis revealed a significantly lower conformational stability compared to natural TIM barrels. In a modular approach, a collection of stabilized variants (DeNovoTIMs) was designed by improving hydrophobic packing [ 86 •• ]. Structural and folding analysis showed that epistatic effects allow navigating an unexplored region of the stability landscape of natural proteins.…”

Section: Learning From Nature Towards Protein Designmentioning

confidence: 99%

Evolution, folding, and design of TIM barrels and related proteins

Romero‐Romero

Kordes

Michel

et al. 2021

Current Opinion in Structural Biology

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Highlights In-depth sequence analysis reveals that the protein fold universe is more evolutionarily connected than previously assumed. Short ancestral fragments are observed to have propagated to many modern proteins and hint at possible evolutionary pathways. Experimental reconstruction of such events by chimeragenesis and directed evolution allows to test evolutionary relationships. Detailed knowledge of folding landscapes helps to understand evolutionary history and improve protein engineering. The ubiquitous and versatile TIM-barrel fold is a model system to explore evolution, folding, and design.

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The stability landscape ofde novoTIM barrels explored by a modular design approach

Cited by 6 publications

References 67 publications

Protein Sequence Design with a Learned Potential

Protein Sequence Design with a Learned Potential

Extension of a de novo TIM barrel with a rationally designed secondary structure element

Evolution, folding, and design of TIM barrels and related proteins

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