Protein design: Past, present, and future

Regan, Lynne; Caballero, Diego; Hinrichsen, Michael; Virrueta, Alejandro; Williams, Danielle M.; O’Hern, Corey S.

doi:10.1002/bip.22639

Cited by 41 publications

(31 citation statements)

References 114 publications

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“…The protein core has long been known to determine protein stability and provide the driving force for folding. [37][38][39][40][41][42][43][44][45] Additionally, in our previous work, we have found that several features of core packing are universal among well-folded experimental structures, such as the repacking predictability of core residue side chain placement, core packing fraction, and distribution of core void space. [46][47][48][49][50][51] This work suggests that analysis of core residue placement and packing in proteins more generally should be effective in determining the accuracy of protein decoys.…”

Section: Introductionmentioning

confidence: 87%

Using physical features of protein core packing to distinguish real proteins from decoys

et al. 2020

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The ability to consistently distinguish real protein structures from computationally generated model decoys is not yet a solved problem. One route to distinguish real protein structures from decoys is to delineate the important physical features that specify a real protein. For example, it has long been appreciated that the hydrophobic cores of proteins contribute significantly to their stability. We used two sources to obtain datasets of decoys to compare with real protein structures: submissions to the biennial Critical Assessment of protein Structure Prediction competition, in which researchers attempt to predict the structure of a protein only knowing its amino acid sequence, and also decoys generated by 3DRobot, which have user‐specified global root‐mean‐squared deviations from experimentally determined structures. Our analysis revealed that both sets of decoys possess cores that do not recapitulate the key features that define real protein cores. In particular, the model structures appear more densely packed (because of energetically unfavorable atomic overlaps), contain too few residues in the core, and have improper distributions of hydrophobic residues throughout the structure. Based on these observations, we developed a feed‐forward neural network, which incorporates key physical features of protein cores, to predict how well a computational model recapitulates the real protein structure without knowledge of the structure of the target sequence. By identifying the important features of protein structure, our method is able to rank decoy structures with similar accuracy to that obtained by state‐of‐the‐art methods that incorporate many additional features. The small number of physical features makes our model interpretable, emphasizing the importance of protein packing and hydrophobicity in protein structure prediction.

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Section: Introductionmentioning

confidence: 87%

Using physical features of protein core packing to distinguish real proteins from decoys

et al. 2020

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“…Advancements introduced over the years by these and other researchers have aimed to improve the treatment of a range of physical effects towards a more realistic representation of proteins, including modeling backbone flexibility (9), better accounting for side-chain degrees of freedom (10,11), treatment of bulk-solvation and individual-water effects (12)(13)(14), consideration of ensemble properties of structure (15)(16)(17)(18), scoring function improvements (3,19), modeling the unfolded state (15,20,21), and others. Several recent reviews provide excellent summaries of achievements and challenges in CPD, including by Donald and co-workers (22) (a thorough analysis of algorithmic aspects), by Ilan Samish and Regan et al (23,24) (placing current work in a historical perspective), and Woolfson et al (25) (a commentary on the implications of the ability to design novel structures). Nevertheless, despite all of these developments, and many examples of successful designs in the literature (18,(26)(27)(28)(29)(30)(31)(32)(33)(34)(35), it is still the case that CPD methods are not robust.…”

Section: Introductionmentioning

confidence: 99%

A general-purpose protein design framework based on mining sequence-structure relationships in known protein structures

Zhou

Panaitiu

Grigoryan

2018

Preprint

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The ability to routinely design functional proteins, in a targeted manner, would have enormous implications for biomedical research and therapeutic development. Computational protein design (CPD) offers the potential to fulfill this need, and though recent years have brought considerable progress in the field, major limitations remain. Current state-of-the-art approaches to CPD aim to capture the determinants of structure from physical principles. While this has led to many successful designs, it does have strong limitations associated with inaccuracies in physical modeling, such that a robust general solution to CPD has yet to be found. Here we propose a fundamentally novel design framework-one based on identifying and applying patterns of sequence-structure compatibility found in known proteins, rather than approximating them from models of inter-atomic interactions. Specifically, we systematically decompose the target structure to be designed into structural building blocks we call TERMs (tertiary motifs) and use rapid structure search against the Protein Data Bank (PDB) to identify sequence patterns associated with each TERM from known protein structures that contain it. These results are then combined to produce a sequence-level pseudo-energy model that can score any sequence for compatibility with the target structure. This model can then be used to extract the optimal-scoring sequence via combinatorial optimization or otherwise sample the sequence space predicted to be well compatible with folding to the target. Here we carry out extensive computational analyses, showing that our method, which we dub dTERMen (design with TERM energies): 1) produces native-like sequences given native crystallographic or NMR backbones, 2) produces sequence-structure compatibility scores that correlate with thermodynamic stability, and 3) is able to predict experimental success of designed sequences generated with other methods, and 4) designs sequences that are found to fold to the desired target by structure prediction more frequently than sequences designed with an atomistic method. As an experimental validation of dTERMen, we perform a total surface redesign of Red Fluorescent Protein mCherry, marking a total of 64 residues as variable. The single sequence identified as optimal by dTERMen harbors 48 mutations relative to mCherry, but nevertheless folds, is monomeric in solution, exhibits similar stability to chemical denaturation as mCherry, and even preserves the fluorescence property. Our results strongly argue that the PDB is now sufficiently large to enable proteins to be designed by using only examples of structural motifs from unrelated proteins. This is highly significant, given that the structural database will only continue to grow, and signals the possibility of a whole host of novel data-driven CPD methods. Because such methods are likely to have orthogonal strengths relative to existing techniques, they could represent an important step towards removing remaining barriers to robust CPD.

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“…11 As such, four-helix bundles have become key targets and scaffolds for de novo protein design. [12][13][14][15][16][17][18] Usually, these comprise amphipathic a helices. These helices assemble via their hydrophobic faces to form bundles with the hydrophobic side chains buried in a consolidated core.…”

Section: Introductionmentioning

confidence: 99%

Navigating the structural landscape ofde novoα–helical bundles

Rhys

Wood

Beesley

et al. 2018

Preprint

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The association of amphipathic a helices in water leads to a-helical-bundle protein structures. However, the driving force for this-the hydrophobic effect-is not specific and does not define the number or the orientation of helices in the associated state. Rather, this is achieved through deeper sequence-to-structure relationships, which are increasingly being discerned. For example, for one structurally extreme but nevertheless ubiquitous class of bundle-the a-helical coiled coils-relationships have been established that discriminate between all-parallel dimers, trimers and tetramers. Association states above this are known, as are antiparallel and mixed arrangements of the helices. However, these alternative states are less-well understood. Here, we describe a synthetic-peptide system that switches between parallel hexamers and various up-down-up-down tetramers in response to single-amino-acid changes and solution conditions. The main accessible states of each peptide variant are characterized fully in solution and, in most cases, to high-resolution X-ray crystal structures. Analysis and inspection of these structures helps rationalize the different states formed. This navigation of the structural landscape of a-helical coiled coils above the dimers and trimers that dominate in nature has allowed us to design rationally a well-defined and hyperstable antiparallel coiled-coil tetramer (apCC-Tet). This robust de novo protein provides another scaffold for further structural and functional designs in protein engineering and synthetic biology.

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Protein design: Past, present, and future

Cited by 41 publications

References 114 publications

Using physical features of protein core packing to distinguish real proteins from decoys

Using physical features of protein core packing to distinguish real proteins from decoys

A general-purpose protein design framework based on mining sequence-structure relationships in known protein structures

Navigating the structural landscape ofde novoα–helical bundles

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