Recovery of protein structure from contact maps.

By Michele Vendruscolo, Edo Kussell, Eytan Domany. 1997

BACKGROUND Prediction of a protein's structure from its amino acid sequence is a key issue in molecular biology. While dynamics, performed in the space of two-dimensional contact maps, eases the necessary conformational search, it may also lead to maps that do not correspond to any real three-dimensional structure. To remedy this, an efficient procedure is needed to reconstruct three-dimensional conformations from their contact maps. RESULTS We present an efficient algorithm to recover the three-dimensional structure of a protein from its contact map representation. We show that when a physically realizable map is used as target, our method generates a structure whose contact map is essentially similar to the target. furthermore, the reconstructed and original structures are similar up to the resolution of the contact map representation. Next, we use nonphysical target maps, obtained by corrupting a physical one; in this case, our method essentially recovers the underlying physical map and structure. Hence, our algorithm will help to fold proteins, using dynamics in the space of contact maps. Finally, we investigate the manner in which the quality of the recovered structure degrades when the number of contacts is reduced. CONCLUSIONS The procedure is capable of assigning quickly and reliably a three-dimensional structure to a given contact map. It is well suited for use in parallel with dynamics in contact map space to project a contact map onto its closest physically allowed structural counterpart.

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Citations in the corpus

Protein 3D Structure Computed from Evolutionary Sequence Variation (2011, background): “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].”
Improved Contact Predictions Using the Recognition of Protein Like Contact Patterns (2014, background): “Knowing which residues in a protein interact with each other provides sufficient information to predict the structure of a protein [8,9].”
Evaluation of residue-residue contact predictions in CASP9 (2011, methodology): “Several algorithms for deriving an approximate structure of a protein from its contact map have been developed,3–7 reaching different levels of accuracy.”
Optimal contact definition for reconstruction of Contact Maps (2010, background): “A few studies [24-26] have tried to evaluate the accuracy of the existing methods but they all lack in completeness of the test set and thorough assessment of the different parameters or do not provide fully realistic protein models but only Cα traces.”
Prediction of contact maps with neural networks and correlated mutations. (2001, methodology): “Provided that residue contacts are known for a protein sequence, the major features of its 3D structure could be deduced by applying different reconstruction methods (Bohr et al., 1993; Vendruscolo et al., 1997).”
Folding non-homologous proteins by coupling deep-learning contact maps with I-TASSER assembly simulations (2021, methodology): “Because of their power in dictating protein folds, residue-residue contacts were employed to address the problem of template-free structure modeling several decades ago (Gobel et al., 1994; Vendruscolo et al., 1997).”
Prediction of contact maps by GIOHMMs and recurrent neural networks using lateral propagation from all four cardinal corners (2002, methodology): “…for the third step of recovering 3D coordinates from contact maps, several algorithms have been described in the NMR literature (Nilges et al., 1988a,b) and elsewhere (Vendruscolo et al., 1997), using distance geometry and stochastic optimization techniques, that seem to work reasonably well.”
Defining an Essence of Structure Determining Residue Contacts in Proteins (2009, methodology): “The other existing implementations of 3D reconstruction from contact maps are based on methods such as Discrete Molecular Dynamics (DMD) [16], singular value decomposition [2,17].”
3D Protein Structure Predicted from Sequence (2011, unknown): “Others have shown that given sufficient correct (true positive) contacts combined with a lack of incorrect (false positive) contacts, predicted contacts can be implemented as residue-‐residue distance restraints to ”
Deep learning methods in protein structure prediction (2020, methodology): “lem [92], heuristic methods have been devised to solve it approximately [89,93,94] and optimised for computational efficiency [90].”
Machine Learning Methods for Protein Structure Prediction (2008, methodology): “tact maps by using Monte Carlo methods [66], [82] and incorporating experimentally determined contacts or contacts ex-”
Deducing high-accuracy protein contact-maps from a triplet of coevolutionary matrices through deep residual convolutional networks (2020, background): “The idea of developing sequence-based contact-map prediction to assist ab initio protein structure prediction is, however, not new, which can be traced back to at least 25 years ago [7,8].”
SCRATCH: a protein structure and structural feature prediction server (2005, methodology): “We include a contact-map energy term (13) based on a binary map derived from the matrix of contact probabilities predicted by CMAPpro.”