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3D Motifs

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From Protein Structure to Function with Bioinformatics

Abstract

Three-dimensional (3D) motifs are patterns of local structure associated with function, typically based on residues in binding or catalytic sites. Protein structures of unknown function can be annotated by comparing them to known 3D motifs. Many methods have been developed for identifying 3D motifs and for searching structures for their occurrence. Approaches vary in the type and amount of input evidence, how the motifs are described and matched, whether the results include a measure of statistical significance, and how the motifs relate to function. Compared to algorithm development, less progress has been made in providing publicly searchable databases of 3D motifs that are both functionally specific and cover a broad range of functions. A roadblock has been the difficulty of generating detailed structure-function classifications; instead, automated, large-scale studies have relied upon pre-existing classifications of either structure or function. Complementary to 3D motif methods are approaches focused on molecular surface descriptions, global structure (fold) comparisons, predicting interactions with other macromolecules, and identifying physiological substrates by docking databases of small molecules.

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Abbreviations

3D:

Three-dimensional

CSA:

Catalytic Site Atlas

DRESPAT:

Detection of REcurring Sidechain PATterns

EC:

Enzyme Commission

FFF:

Fuzzy Functional Form

GASPS:

Genetic Algorithm Search for Patterns in Structures

GO:

Gene Ontology

HMM:

Hidden Markov Model

nr-PDB:

Non-redundant PDB

NP:

Nonpolynomial (scaling)

NOE:

Nuclear Overhauser Effect

PAR-3D:

Protein Active site Residues using 3-Dimensional structural motifs

PDB:

Protein Data Bank

PINTS:

Patterns in Non-homologous Tertiary Structures

RMSD:

Root-mean-square Deviation

S-BLEST:

Structure-Based Local Environment Search Tool

SCOP:

Structural Classification of Proteins

SOIPPA:

Sequence Order-Independent Profile-Profile Alignment

SPASM:

SPatial Arrangements of Sidechains and Mainchains

TESS:

TEmplate Search and Superposition

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Acknowledgements

We acknowledge support from NIH GM60595 and NSF DBI-0234768. Molecular graphics were produced with the UCSF Chimera package from the Resource for Biocomputing, Visualization, and Informatics at the University of California, San Francisco (supported by NIH P41-GM103311). We thank Jacquelyn Fetrow and Stacy Knutson (Wake Forest University) for providing Fig. 11.5 as an example of a result from their FFF/DASP/PASS motif analysis software. We gratefully acknowledge Dan Kirshner for enlightening discussions and a critical reading of the manuscript.

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Authors and Affiliations

  1. Lawrence Livermore National Laboratory (LLNL) Division of Physical and Life Sciences Directorate, Biotechnology and Biosciences Division, 7000 East Avenue, Livermore, CA, 94550-9234, USA

    Jerome P. Nilmeier

  2. Department of Pharmaceutical Chemistry, University of California San Francisco (UCSF), 600 16th Street, San Francisco, CA, 94158-2517, USA

    Elaine C. Meng, Benjamin J. Polacco & Patricia C. Babbitt

  3. UCSF Department of Biopharmaceutical Sciences, 1700 4th Street, San Francisco, CA, 94158-2330, USA

    Patricia C. Babbitt

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  1. Jerome P. Nilmeier
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  2. Elaine C. Meng
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  4. Patricia C. Babbitt
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Correspondence to Jerome P. Nilmeier .

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  1. Institute of Integrative Biology, University of Liverpool, Liverpool, United Kingdom

    Daniel J. Rigden

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Nilmeier, J.P., Meng, E.C., Polacco, B.J., Babbitt, P.C. (2017). 3D Motifs. In: J. Rigden, D. (eds) From Protein Structure to Function with Bioinformatics. Springer, Dordrecht. https://doi.org/10.1007/978-94-024-1069-3_11

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