De novo design of a non-local β-sheet protein with high stability and accuracy

Marcos, Enrique Sánchez; Chidyausiku, Tamuka M.; McShan, Andrew C.; Evangelidis, Thomas; Nerli, Santrupti; Carter, Lauren; Nivón, Lucas G.; Davis, Audrey L.; Oberdorfer, Gustav; Tripsianes, Konstantinos; Sgourakis, Nikolaos G.; Baker, David

doi:10.1038/s41594-018-0141-6

Cited by 114 publications

(127 citation statements)

References 55 publications

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“…Computational design has been successful in mimicking the ability of evolution to generate diverse protein structures spanning helical [6][7][8][9][10] , alpha-beta [11][12][13] and beta-sheet 14,15 fold topologies, including novel folds 16 . However, most design methods do not include explicit mechanisms to vary geometric features within a topology.…”

Section: Main Textmentioning

confidence: 99%

“…Previous key achievements in de novo design [11][12][13][14][15]20 focused on designing one or a few structures for diverse non-helical-bundle topologies by deriving design rules for specific topologies to identify the most favorable geometries. Proteins designed by this topology-centric strategy have pre-defined secondary structure sizes and loop torsions that are ideal to their topologies.…”

Section: Main Textmentioning

confidence: 99%

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Expanding the space of protein geometries by computational design ofde novofold families

Pan

Thompson

Zhang

et al. 2020

Preprint

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Naturally occurring proteins use a limited set of fold topologies, but vary the precise geometries of structural elements to create distinct shapes optimal for function. Here we present a computational design method termed LUCS that mimics nature’s ability to create families of proteins with the same overall fold but precisely tunable geometries. Through near-exhaustive sampling of loop-helix-loop elements, LUCS generates highly diverse geometries encompassing those found in nature but also surpassing known structure space. Biophysical characterization shows that 17 (38%) out of 45 tested LUCS designs were well folded, including 16 with designed non-native geometries. Four experimentally solved structures closely match the designs. LUCS greatly expands the designable structure space and provides a new paradigm for designing proteins with tunable geometries customizable for novel functions.One Sentence SummaryA computational method to systematically sample loop-helix-loop geometries expands the structure space of designer proteins.

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Section: Main Textmentioning

confidence: 99%

Section: Main Textmentioning

confidence: 99%

Expanding the space of protein geometries by computational design ofde novofold families

Pan

Thompson

Zhang

et al. 2020

Preprint

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show abstract

“…Marcos, E., et al identified the rules for β-arches, in which loops connect two β-strands belonging to different β-sheets, and succeeded in designing a non-local β-sheet protein, jellyroll structure, de novo (Fig. 4) [36]. Likewise, Doyle, L., et al and Brunette, T. J., et al designed α-helical tandem repeat proteins successfully de novo (Fig.…”

Section: The Rules For Designing Ideal Proteinsmentioning

confidence: 99%

Consistency principle for protein design

Koga

2019

BIOPHYSICS

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Protein design holds promise for applications such as the control of cells, therapeutics, new enzymes and proteinbased materials. Recently, there has been progress in rational design of protein molecules, and a lot of attempts have been made to create proteins with functions of our interests. The key to the progress is the development of methods for controlling desired protein tertiary structures with atomic-level accuracy. A theory for protein folding, the consistency principle, proposed by Nobuhiro Go in 1983, was a compass for the development. Anfinsen hypothesized that proteins fold into the free energy minimum structures, but Go further considered that local and non-local interactions in the free energy minimum structures are consistent with each other. Guided by the principle, we proposed a set of rules for designing ideal protein structures stabilized by consistent local and non-local interactions. The rules made possible designs of amino acid sequences with funnel-shaped energy landscapes toward our desired target structures. So far, various protein structures have been created using the rules, which demonstrates significance of our rules as intended. In this review, we briefly describe how the consistency principle impacts on our efforts for developing the design technology.Key words: computational protein design, ideal proteins, funnel-shaped energy landscapes, consistent local and non-local interactions, Go modelUnderstanding of protein folding is important to develop the methodology for creating our desired proteins. Anfinsen hypothesized that proteins fold into the free energy minimum structures [1]. However, the folding problem-How do amino acid sequences determine the folded structures?has been a long-standing problem for more than a half century. Researches for protein folding or structure prediction from amino acid sequences have attempted to address the problem by studying complicated proteins created by nature spending billions of years, which have energetically unfavorable non-ideal features such as kinked α-helices, bulged β-strands and buried polar residues. Protein design studies provide an alternative approach to tackle the problem by creating simple protein structures not having such unfavorable features from scratch with hypotheses about protein folding and experimentally testing how the designs fold.Protein design expands the possibility of developments for therapeutics, biosensors, materials, etc. Recently there has been great progress in computational design of protein structures. The basic idea that underlies the progress is the rules we discovered relating secondary structure patterns to tertiary motifs, which make it possible to design Go's proposed ideal protein structures. In this review, we describe how our rules were discovered in the history of protein design and folding studies.

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“…De novo protein design is said to have come of age [1]. From the early de novo proteins confirmed by high-resolution structures [2][3][4], the field has advanced rapidly with new scaffolds covering all-a [5,6 ,7], all-b [8], and mixed-a/b and a + b structural space [9,10 ,11]. In addition, side-chain constellations can be controlled exquisitely to introduce networks of hydrogen bonds throughout target structures [12], which, in turn, can improve the design and characterisation of de novo membrane proteins [13].…”

Section: Introductionmentioning

confidence: 99%

Towards functional de novo designed proteins

Dawson

Rhys

Woolfson

2019

Current Opinion in Chemical Biology

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Our ability to design completely de novo proteins is improving rapidly. This is true of all three main approaches to de novo protein design, which we define as: minimal, rational and computational design. Together, these have delivered a variety of protein scaffolds characterised to high resolution. This is truly impressive and a major advance from where the field was a decade or so ago. That all said, significant challenges in the field remain. Chief amongst these is the need to deliver functional de novo proteins. Such designs might include selective and/or tight binding of specified small molecules, or the catalysis of entirely new chemical transformations. We argue that, whilst progress is being made, solving such problems will require more than simply adding functional side chains to extant de novo structures. New approaches will be needed to target and build structure, stability and function simultaneously. Moreover, if we are to match the exquisite control and subtlety of natural proteins, design methods will have to incorporate multi-state modelling and dynamics. This will require more than black-box methodology, specifically increased understanding of protein conformational changes and dynamics will be needed.

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De novo design of a non-local β-sheet protein with high stability and accuracy

Cited by 114 publications

References 55 publications

Expanding the space of protein geometries by computational design ofde novofold families

Expanding the space of protein geometries by computational design ofde novofold families

Consistency principle for protein design

Towards functional de novo designed proteins

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