ExploreTurns, MapTurns, and ProfileTurn: tools for the exploration, analysis and classification of beta turns and structured loops in proteins

 

This site hosts a suite of tools for the study of four-residue (beta) turns and structured loops in proteins. Beta turns are the most common secondary structures in proteins after alpha helices and beta sheets, and they play key structural and functional roles (see the link below). Since beta turns constitute close to two-thirds of protein loops and the tools also encompass the backbone (BB) neighborhoods of turns, they provide the most comprehensive and detailed picture available of the recurrent BB and side-chain (SC) structures in loops, which represent about half of all protein structure.

ExploreTurns [1,2] is a facility for the exploration, analysis, geometric tuning and retrieval of beta turns and the motifs and contexts in which they occur in a redundancy-screened, PDB-derived database. The tool supports structure selection using a new set of geometric descriptors for beta turns [3,4] that complement the BB dihedral-angle-based (Ramachandran-space) turn classification systems and enable Euclidean-space structural discrimination within turn types [5] or BB clusters [6]. ExploreTurns also includes an interpreter for a new "compound turn" (CT) notation which classifies all short H-bonded loop motifs (such as the Schellman [7], beta-bulge [8] and alpha-beta [9] loops) in terms of their component "simple" H-bonded turns and has been applied to detect multiple new loop motifs, including short and "double" Schellman loops, a large family of beta-bulge loops with a range of geometries and H-bond topologies, and other structures.

An analysis of example structures from the AFDB50 database of ~52 million predicted chains [11], which is about 3,000 times larger than the PDB at comparable redundancy, suggests that AlphaFold predicts all types of H-bonded loop motifs detected by ExploreTurns in the PDB, in numbers approximately proportional to the ratio of the database sizes, and new motifs have also been detected. Subject to the caveat that they represent predictions, the AF models should provide much more complete coverage of the BB H-bond variants of loop motifs and the SC interactions which shape them, further supporting the design of loop-bearing structures such as beta hairpins, which are common targets in protein and peptide engineering.
MapTurns [10] is a server for motif maps, which are interactive, 3D graphical conformational heatmaps of the BB and SC structure and H-bonding in beta turns and their BB neighborhoods. Maps characterize many new SC motifs, provide rationalizations of sequence preferences, and support mutational analysis and the general study of SC interactions, and they should prove useful in applications, such as protein design, which can benefit from the comprehensive and detailed picture of structure and interaction which they provide.
ProfileTurn profiles a turn uploaded by the user and evaluates the compatibility between its geometry and sequence motif content.
These tools incorporate a turn-local coordinate system [3,4] that implicitly aligns all beta turns and supports the visualization, comparison and analysis of the wide range of turn geometries and the motifs and contexts in which turns occur. Extensive online help is provided. For best performance and appearance, browse with Chrome or Edge.

Protein Society and ISMB poster  

A poster describing ExploreTurns, MapTurns and ProfileTurn presented at the Protein Society's 2023 and 2024 symposia and at the 3D structure (3DSig) COSI of the 2024 and 2025 ISMB conferences.


BioVis MapTurns poster  

A poster describing MapTurns presented at the BioVis COSI of ISMB 2024.


Roles of beta turns in proteins  

An overview of key roles played by beta turns in proteins.


References

1)  Newell NE. 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. Protein Science. 2025. doi: https://doi.org/10.1002/pro.70046

2)  Newell NE. 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. bioRxiv. 2024. doi: https://doi.org/10.1101/2024.01.01.573820

3)  Newell NE. Geometric descriptors for beta turns. Protein Science. 2024. doi:https://doi.org/10.1002/pro.5159

4)  Newell NE. A geometric parameterization for beta turns. bioRxiv. 2024. doi: https://doi.org/10.1101/2024.01.01.573818

5)  Hutchinson EG, Thornton JM. PROMOTIF--A program to identify and analyze structural motifs in proteins. Protein Science. 1996;5(2):212-220.

6)  Shapovalov M, Vucetic S, Dunbrack RL. A new clustering and nomenclature for beta turns derived from high-resolution protein structures. PLOS Computational Biology. 2019;15(3).

7)  Schellman C. The R-L conformation at the ends of helices. In: Jaenicke R, editor. Protein folding. New York: Elsevier/North-Holland; 1980. p. 53-61.

8)  Milner-White EJ. Beta-bulges within loops as recurring features of protein structure. Biochim Biophys Acta. 1987;911(2):261-5.

9)  Leader DP, Milner-White EJ. Motivated Proteins: A web application for studying small three-dimensional protein motifs. BMC Bioinformatics. 2009;10:60.

10)  Newell NE. MapTurns: mapping the structure, H-bonding and contexts of beta turns in proteins. Bioinformatics. 2025. doi: https://doi.org/10.1093/bioinformatics/btae741

11)  Barrio-Hernandez IB, Yeo J, Janes J, Mirdita M et al. Clustering predicted structures at the scale of the known protein universe. Nature. 2023;622:637-645.