Global Fold Determination from a Small Number of Distance Restraints

https://doi.org/10.1006/jmbi.1995.0436Get rights and content

Abstract

We have designed a distance geometry-based method for obtaining the tertiary fold of a protein from a limited number of structure-specific distance restraints and the secondary structure assignment. Interresidue distances were predicted from patterns of conserved hydrophobic amino acids deduced from multiple alignments. A simple model chain representing the protein was then folded by projecting its distance matrix into Euclidean spaces with gradually decreasing dimensionality until a final three-dimensional embedding was achieved. Tangled conformations produced by the projection steps were eliminated using a novel filtering algorithm. Information on various aspects of protein structure such as accessibility and chirality was incorporated into the conformation refinement, increasing the robustness of the algorithm. The method successfully identified the correct folds of three small proteins from a small number of restraints, indicating that it could serve as a useful computational tool in protein structure determination from NMR data.f2

References (0)

Cited by (120)

  • Algorithms for protein design

    2022, Advances in Protein Chemistry and Structural Biology
    Citation Excerpt :

    The distribution function can incorporate information obtained from any relevant biomolecular force field (Talluri, 2017). Subsequently, embedding is used to project data from a higher level space to three dimensional space for generation of the Cartesian coordinates of biopolymers (Aszodi, Gradwell, & Taylor, 1995; Havel, 1990). The resulting conformation is then subjected to conventional structure refinement procedures (Wagner et al., 1987).

  • Protein domain identification methods and online resources

    2021, Computational and Structural Biotechnology Journal
  • Protein contact map prediction using multi-stage hybrid intelligence inference systems

    2012, Journal of Biomedical Informatics
    Citation Excerpt :

    Therefore, developing new machine learning approaches or improving current approaches to predict protein structure can decrease this gap. Earlier studies indicate that developing an accurate protein contact map predictor will be very helpful in the reconstruction of protein 3D structure [6–8,13]. Accordingly, much research is implemented in this problem due to the current low accuracy [9–11].

  • Direct correlation analysis improves fold recognition

    2011, Computational Biology and Chemistry
View all citing articles on Scopus
f1

Corresponding author

f2

Abbreviations used: 2D and 3D, two- and three-dimensional; NOE, nuclear Overhauser effect; PDB, Protein Data Bank.

View full text