Fidelity of the Protein Structure Reconstruction from Inter-Residue Proximity Constraints

Abstract

Inter-residue proximity constraints obtained in such experiments as cross-linking/mass spectrometry are important sources of information for protein structure determination. A central question in structure determination using these constraints is, What is the minimal number of inter-residue constraints needed to determine the fold of a protein? It is also unknown how the different structural aspects of constraints differentiate their ability in determining the native fold and whether there is a rational strategy for selecting constraints that feature higher fidelity in structure determination. To shed light on these questions, we study the fidelity of protein fold determination using theoretical inter-residue proximity constraints derived from protein native structures and the effect of various subsets of such constraints on fold determination. We show that approximately 70% randomly selected constraints are sufficient for determining the fold of a domain (with an average root-mean-square deviation of e3.4 Å from their native structures). We find that random constraint selection often outperforms the rational strategy that predominantly favors the constraints representing global structural features. To uncover a strategy for constraint selection for the optimal structure determination, we study the role of the topological properties of these constraints. Interestingly, we do not observe any correlation between various simple topological properties of the selected constraints, emphasizing different global and local structural features, and the performance of these constraints, suggesting that accurate protein structure determination relies on a composite of global and local structural information.

Designing evolvable libraries using multi-body potentials

“…A similar conclusion that present PDB data are already complete enough to solve the protein structure prediction problem has been reached using homology-based arguments [34]. It has already been shown that a small subset of distance constraints can accurately define the structure [13]. Hence the development of algorithms to define adaptive and essential subsets of constraints is the most promising way forward.…”

“…A binary contact map describes an ensemble of protein structures as a network of contacts or distance constraints (see Box 1.). Currently, three-dimensional structures can be reconstructed from contact maps at an accuracy of approximately 2 Å Ca rmsd [13]. The result is not a single structure but, like models from NMR data, an entire ensemble of conformations consistent with the given constraints.…”

“…Additional evidence from both sequence and the structure-based paradigms show that sparse subnetworks of contacts or couplings can provide sufficient information to define the native structure. In order to evaluate the role of such minimalistic constraint-sets in defining a protein fold, Chen and coworkers used random subsets of contacts from the native contact map [13]. Before discussing essential subsets of constraints, we first look into the current state of sampling sequences from a probabilistic framework.…”

“…The first attempt to characterize an essential subset, Dokholyan and co-workers investigated the behaviour of random subsets from protein structures [13,15]. From the randomly selected contacts the 3D structure was reconstructed using Discrete Molecular Dynamics (DMD).…”

Community structure description in amino acid interaction networks

“…The authors of some reports (Bagler and Sinha, 2007;Chen et al, 2007;Vassura et al, 2008b) propose different methods to predict amino acid interaction networks from the primary sequence. The authors try to reconstruct the protein backbones but not the entire tertiary structures.…”