Automatic identification of discrete substates in proteins: Singular value decomposition analysis of time‐averaged crystallographic refinements

Abstract

The singular value decomposition (SVD) provides a method for decomposing a molecular dynamics trajectory into fundamental modes of atomic motion. The right singular vectors are projections of the protein conformations onto these modes showing the protein motion in a generalized low-dimensional basis. Statistical analysis of the right singular vectors can be used to classify discrete configurational substates in the protein. The configuration space portraits formed from the right singular vectors can also be used to visualize complex high-dimensional motion and to examine the extent of configuration space sampling by the simulation.