β‐Bulges: Extensive structural analyses of β‐sheets irregularities

Craveur, Pierrick; Joseph, Agnel Praveen; Rebehmed, Joseph; Brevern, Alexandre G. de

doi:10.1002/pro.2324

Cited by 34 publications

(32 citation statements)

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“…We then carried out RosettaDesign calculations (24) to favor amino acid identities and sidechain conformations with low-energy, tight packing and high sequence-structure compatibility. We hypothesized that β-bulge positions could be specified at the sequence level solely by changing the normal alternating pattern of polar and hydrophobic amino acids (more complex patterns are observed in native structures (see refs (19, 25) and fig. S4)) — in a β-bulge, unlike regular strands, two successive residues point in the same direction (Fig.…”

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Principles for designing proteins with cavities formed by curved β sheets

Marcos

Basanta

Chidyausiku

et al. 2017

Science

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Active sites and ligand binding cavities in native proteins are often formed by curved β-sheets, and the ability to control β-sheet curvature would allow design of binding proteins with cavities customized to specific ligands. Towards this end, we investigated the mechanisms controlling β-sheet curvature by studying the geometry of β-sheets in naturally occurring protein structures and * Correspondence to: dabaker@u.washington.edu. † These authors contributed equally to this work. Supplementary Materials: Materials and MethodsFigs. S1 to S22 Tables S1 to S7 Input files and command lines for design calculations HHS Public Access Author Manuscript Author ManuscriptAuthor ManuscriptAuthor Manuscript folding simulations. The principles emerging from this analysis were used to de novo design a series of proteins with curved β-sheets topped with a-helices. NMR and crystal structures of the designs closely match the computational models, showing that β-sheet curvature can be controlled with atomic-level accuracy. Our approach enables the design of proteins with cavities and provides a route to custom design ligand binding and catalytic sites.Ligand binding proteins with curved β-sheets surrounding the binding pocket, as in the NTF2-like, β-barrel, and jelly roll folds, play key roles in molecular recognition, metabolic pathways and cell signaling. Approaches to designing small molecule binding proteins and enzymes to date have started by searching for native protein scaffolds with ligand binding pockets with roughly the right geometry, and then redesigning the surrounding residues to optimize interactions with the small molecule. While this approach has yielded new binding proteins and catalysts (1-5), it is not optimal: there may be no naturally occurring scaffold with a pocket with the correct geometry, and introduction of mutations in the design process may change the pocket structure (6, 7). Building de novo proteins with custom-tailored binding sites could be a more effective strategy, but this remains an outstanding challenge (8-11). De novo protein design has recently focused on proteins with ideal backbone structures (12-16) (straight helices, uniform β-strands and short loops; see ref (17) for a recent exception) and optimal core sidechain packing, but the binding pockets of naturally occurring proteins lie on concave surfaces formed by non-ideal features such as kinked helices, curved β-sheets or long loops. The design of proteins with concave surfaces requires examination of how such irregular structural features can be programmed into the amino acid sequence.We begin by analyzing how classic (18, 19) β-bulges (irregularities in the pleating of edge strands) and register shifts (local termination of strand pairing) coupled with intrinsic β-strand geometry induce curvature in antiparallel β-sheets (20, 21). We quantify the curvature of an edge strand making an antiparallel pairing with a second strand by the bend angle (Fig. 1A). The absolute value of the bend angle (α) at residue i is the angle bet...

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confidence: 99%

Principles for designing proteins with cavities formed by curved β sheets

Marcos

Basanta

Chidyausiku

et al. 2017

Science

119

125

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“…3 times more proteins with longer times than before). The MD protocol was the same as in the original paper (see [20,24] for details). These new data are used to evaluate the stability of the prediction quality.…”

Section: Resultsmentioning

confidence: 99%

In silico prediction of protein flexibility with local structure approach

et al. 2019

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Flexibility is an intrinsic essential feature of protein structures, directly linked to their functions. To this day, most of the prediction methods use the crystallographic data (namely B-factors) as the only indicator of protein's inner flexibility and predicts them as rigid or flexible. PredyFlexy stands differently from other approaches as it relies on the definition of protein flexibility (i) not only taken from crystallographic data, but also (ii) from Root Mean Square Fluctuation (RMSFs) observed in Molecular Dynamics simulations. It also uses a specific representation of protein structures, named Long Structural Prototypes (LSPs). From Position-Specific Scoring Matrix, the 120 LSPs are predicted with a good accuracy and directly used to predict (i) the protein flexibility in three categories (flexible, intermediate and rigid), (ii) the normalized B-factors, (iii) the normalized RMSFs, and (iv) a confidence index.Prediction accuracy among these three classes is equivalent to the best two class prediction methods, while the normalized B-factors and normalized RMSFs have a good correlation with experimental and in silico values. Thus, PredyFlexy is a unique approach, which is of major utility for the scientific community. It support parallelization features and can be run on a local cluster using multiple cores.The entire project is available under an open-source license at

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“…Chan et al [33] have systematically classified β-bulges. A revised analysis by Craveur et al [34] describes how β-bulges impart irregularities in β-strand pairing by disrupting alternation of side-chains or terminate the β-strand. Our analysis showed that approximately 18-20 % of indents as well as edges have β-bulges (Table 1).…”

Section: Short Edge Vs Disprotmentioning

confidence: 99%