MapTurns: mapping the structure, H-bonding and contexts of beta turns in proteins

Newell, Nicholas E.

doi:10.1101/2024.01.02.573949

Cited by 3 publications

(3 citation statements)

References 18 publications

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“…The turn-local coordinate system and global alignment derived here resolve the difficulties with visualization inherent in the Ramachandran-space turn representation, providing a natural framework for the comparison and analysis of the wide variety of beta-turn structures and the contexts in which they occur, including H-bonded motifs, ligand-binding/active sites and supersecondary structures. The utility of this framework has been demonstrated by the application of ExploreTurns 6 to the characterization of multiple new short H-bonded loops, the mapping of sequence preferences in Asx N-caps, and the profiling of Schellman loop/beta turn combinations, and the framework also supports the comprehensive maps of sequence motifs produced by MapTurns 14 .…”

Section: Discussionmentioning

confidence: 86%

“…Many of the results presented here were generated with ExploreTurns 6 , a web facility for the comprehensive exploration, analysis, geometric tuning and retrieval of beta turns and their contexts from the dataset compiled for this study. A second tool, MapTurns 14 , produces interactive, 3D conformational heatmaps of the BB and SC structure, H-bonding and contexts associated with sequence motifs in beta turns, and a third facility, ProfileTurn, profiles a turn uploaded by the user and evaluates the compatibility between its geometry and sequence motif content. Extensive online support is available within all three tools, which are available at www.betaturn.com.…”

Section: Introductionmentioning

confidence: 99%

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A geometric parameterization for beta turns

Newell

2024

Preprint

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Beta turns, in which the protein backbone abruptly changes direction over four amino acid residues, are the most common type of protein secondary structure after helices and strands and play key structural and functional roles. Previous work has produced classification systems for turn backbone geometry at multiple levels of precision, but these operate in the Ramachandran space of backbone dihedral angles, and the absence of a local Euclidean-space coordinate system and structural alignment for turns, or of any systematic Euclidean-space characterization of turn backbone shape, presents challenges for the visualization, comparison and analysis of the wide range of turn conformations and the design of turns and the structures that incorporate them. This work derives a local coordinate system that implicitly aligns beta turns, together with a simple geometric parameterization for turn backbone shape that describes modes of structural variation not explicitly captured by existing systems. These modes are shown to be meaningful by the demonstration of clear relationships between parameter values and the electrostatic energy of the common beta-turn H-bond, the overrepresentation of key side-chain motifs, and the structural contexts in which turns are found. Geometric turn parameters, which complement existing Ramachandran-space classifications, can be used to select structures for compatibility with particular side-chain interactions or contexts, and they should prove valuable in applications, such as protein design, where an enhanced Euclidean-space description of turns may improve understanding or performance. The web-based tools ExploreTurns, MapTurns and ProfileTurn, available atwww.betaturn.com, incorporate turn-local coordinates and turn parameters and demonstrate their utility.

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Section: Discussionmentioning

confidence: 86%

Section: Introductionmentioning

confidence: 99%

A geometric parameterization for beta turns

Newell

2024

Preprint

Self Cite

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“…ExploreTurns supports the further investigation of SC motifs by providing direct access to the motif maps generated by the MapTurns 25 tool (available at www.betaturn.com), which enable the comprehensive exploration of the BB and SC structure, H-bonding and contexts associated with single-AA, pair, and many triplet sequence motifs in turns and their BB neighborhoods.…”

Section: Tool Descriptionmentioning

confidence: 99%

ExploreTurns: A web tool for the exploration, analysis, and classification of beta turns and structured loops in proteins; application to beta-bulge and Schellman loops, Asx helix caps, beta hairpins and other hydrogen-bonded motifs

Newell

2024

Preprint

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The most common type of protein secondary structure after the alpha helix and beta strand is the beta turn, in which the backbone (BB) chain abruptly changes direction over four amino acid residues. Existing tools for the study of beta turns characterize their structures exclusively in the Ramachandran space of BB dihedral angles, which presents challenges for the visualization, comparison and analysis of the wide variety of turn geometries. A recent study has introduced a turn-local Euclidean-space coordinate system and a global alignment for turns, along with a set of geometric parameters for their bulk BB shape which describe modes of structural variation not explicitly captured by Ramachandran-space systems. These features, and others, are incorporated here into ExploreTurns, a tool specialized for the exploration, analysis, geometric tuning and retrieval of beta turns and their local contexts which combines the advantages of Ramachandran- and Euclidean-space representations. ExploreTurns is a database selection screen, structure profiler and 3D viewer, integrated onto a single web page, that renders a redundancy-screened, PDB-derived beta-turn dataset structurally addressable by a wide range of criteria describing the turns and their BB neighborhoods. The tool is applied here to a proposed generalization of the beta-bulge loop, a mapping of helix-capping sequence preferences, a conformational profile of the Schellman loop, and an analysis of the depth dependence of turn geometry, and it should prove generally useful in research, education, and applications such as protein design, in which an enhanced Euclidean-space description of turns and structural tuning may improve performance.

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Geometric descriptors for beta turns

Newell

2024

Protein Science

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Beta turns, in which the protein backbone abruptly changes direction over four amino acid residues, are the most common type of protein secondary structure after alpha helices and beta sheets and play key structural and functional roles. Previous work has produced classification systems for turn geometry at multiple levels of precision, but these operate in backbone dihedral‐angle (Ramachandran) space, and the absence of a local Euclidean‐space coordinate system and structural alignment for turns, or of any systematic Euclidean‐space characterization of turn backbone shape, presents challenges for the visualization, comparison and analysis of the wide range of turn conformations and the design of turns and the structures that incorporate them. This work derives a turn‐local coordinate system that implicitly aligns turns, together with a set of geometric descriptors that characterize the bulk BB shapes of turns and describe modes of structural variation not explicitly captured by existing systems. These modes are shown to be meaningful by the demonstration of clear relationships between descriptor values and the electrostatic energy of the beta‐turn H‐bond, the overrepresentations of key side‐chain motifs, and the structural contexts of turns. Geometric turn descriptors complement Ramachandran‐space classifications, and they can be used to select turn structures for compatibility with particular side‐chain interactions or contexts. Potential applications include protein design and other tasks in which an enhanced Euclidean‐space characterization of turns may improve understanding or performance. The web‐based tools ExploreTurns, MapTurns, and ProfileTurn, available at www.betaturn.com, incorporate turn‐local coordinates and turn descriptors and demonstrate their utility.

show abstract

MapTurns: mapping the structure, H-bonding and contexts of beta turns in proteins

Cited by 3 publications

References 18 publications

A geometric parameterization for beta turns

A geometric parameterization for beta turns

ExploreTurns: A web tool for the exploration, analysis, and classification of beta turns and structured loops in proteins; application to beta-bulge and Schellman loops, Asx helix caps, beta hairpins and other hydrogen-bonded motifs

Geometric descriptors for beta turns

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