Structural Energy Landscapes and Plasticity of the Microstates of Apo Escherichia coli cAMP Receptor Protein.
Rati ChkheidzeWilfredo EvangelistaMark Andrew WhiteY Whitney YinJ Ching LeePublished in: Biochemistry (2020)
The theory for allostery has evolved to a modern energy landscape ensemble theory, the major feature of which is the existence of multiple microstates in equilibrium. The properties of microstates are not well defined due to their transient nature. Characterization of apo protein microstates is important because the specific complex of the ligand-bound microstate defines the biological function. The information needed to link biological function and structure is a quantitative correlation of the energy landscapes between the apo and holo protein states. We employed the Escherichia coli cAMP receptor protein (CRP) system to test the features embedded in the ensemble theory because multiple crystalline apo and holo structures are available. Small angle X-ray scattering data eliminated one of the three apo states but not the other two. We defined the underlying energy landscape differences among the apo microstates by employing the computation algorithm COREX/BEST. The same connectivity patterns among residues in apo CRP are retained upon binding of cAMP. The microstates of apo CRP differ from one another by minor structural perturbations, resulting in changes in the energy landscapes of the various domains of CRP. Using the differences in energy landscapes among these apo states, we computed the cAMP binding energetics that were compared with solution biophysical results. Only one of the three apo microstates yielded data consistent with the solution data. The relative magnitude of changes in energy landscapes embedded in various apo microstates apparently defines the ultimate outcome of the cooperativity of binding.
Keyphrases
- binding protein
- escherichia coli
- high resolution
- machine learning
- electronic health record
- multiple sclerosis
- protein protein
- healthcare
- big data
- amino acid
- social media
- molecular dynamics
- artificial intelligence
- protein kinase
- multidrug resistant
- convolutional neural network
- mass spectrometry
- ionic liquid
- molecular dynamics simulations
- biofilm formation