Defining an Essence of Structure Determining Residue Contacts in Proteins

By Rajagopal Sathyapriya, Jose M. Duarte, Henning Stehr, Ioannis Filippis, Michael Lappe. 2009

49 citations recorded by the source corpus.

The network of native non-covalent residue contacts determines the three-dimensional structure of a protein. However, not all contacts are of equal structural significance, and little knowledge exists about a minimal, yet sufficient, subset required to define the global features of a protein. Characterisation of this “structural essence” has remained elusive so far: no algorithmic strategy has been devised to-date that could outperform a random selection in terms of 3D reconstruction accuracy (measured as the Ca RMSD). It is not only of theoretical interest (i.e., for design of advanced statistical potentials) to identify the number and nature of essential native contacts—such a subset of spatial constraints is very useful in a number of novel experimental methods (like EPR) which rely heavily on constraint-based protein modelling. To derive accurate three-dimensional models from distance constraints, we implemented a reconstruction pipeline using distance geometry. We selected a test-set of 12 protein structures from the four major SCOP fold classes and performed our reconstruction analysis. As a reference set, series of random subsets (ranging from 10% to 90% of native contacts) are generated for each protein, and the reconstruction accuracy is computed for each subset. We have developed a rational strategy, termed “cone-peeling” that combines sequence features and network descriptors to select minimal subsets that outperform the reference sets. We present, for the first time, a rational strategy to derive a structural essence of residue contacts and provide an estimate of the size of this minimal subset. Our algorithm computes sparse subsets capable of determining the tertiary structure at approximately 4.8 Å Ca RMSD with as little as 8% of the native contacts (Ca-Ca and Cb-Cb). At the same time, a randomly chosen subset of native contacts needs about twice as many contacts to reach the same level of accuracy. This “structural essence” opens new avenues in the fields of structure prediction, empirical potentials and docking.

Subfields: Computational Theory and Mathematics, Molecular Biology, Spectroscopy

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Citations in the corpus

Protein 3D Structure Computed from Evolutionary Sequence Variation (2011, neutral): “This number is close to those reported by other groups, who used fully correct close residue pairs to impose inexact distances as constraints [50,51,67].”
Optimal contact definition for reconstruction of Contact Maps (2010, background): “This is of special importance if we consider that the reconstruction of contact maps seems to be possible even with sparser contact maps (see [30,31]), which means that contacts even at optimal definitions still seem to contain redundant information.”
3D Protein Structure Predicted from Sequence (2011, unknown): “This number is close to those reported by other groups, who used fully correct close residue pairs to impose inexact distances as constraints [48,49,65].”
ConEVA: a toolbox for comprehensive assessment of protein contacts (2016, background): “In a similar study, introducing a novel cone-peeling algo-rithm, Sathyapriya et al. suggest that as little as 8% of the native contacts are sufficient to determine the tertiary structure [36].”
CONFOLD: Residue-Residue Contact-guided ab initio Protein Folding (2015, background): “…number of predicted contacts needed to obtain best fold Although 99.9% of the proteins in PDB have less than 3 L contacts, much fewer true contacts are sufficient to fold the proteins accurately.24 ,25 However, how many predicted contacts are needed to best fold proteins is still an open question.”
Deep Spatio-Temporal Architectures and Learning for Protein Structure Prediction (2012, background): “While contact map prediction can be viewed as a sub-problem in protein structure prediction, it is well known that it is essentially equivalent to protein structure predictions since 3D structures can be completely recovered from sufficiently large subsets of true contacts [20, 26, 23].”