Normal-Modes-Based Prediction of Protein Conformational Changes Guided by Distance Constraints

Based on the elastic network model, we develop a novel method that predicts the conformational change of a protein complex given its initial-state crystal structure together with a small set of pairwise distance constraints for the end state. The predicted conformational change, which is a linear combination of multiple low-frequency normal modes that are solved from the elastic network model, is computed as a response displacement induced by a perturbation to the system Hamiltonian that incorporates the given distance constraints. For a list of test cases, we find that the computed response displacement overlaps significantly with the measured conformational changes, when only a handful of pairwise constraints are used (≤10). The performance of this method is also shown to be robust against different choices of pairwise distance constraints and errors in their values. This method, if supplied with the experimentally derived distance constraints (for example, from NMR or other spectroscopic measurements), can be applied to the analysis of protein conformational changes toward transient states.