From Selection to Instruction and Back: Competing Conformational Selection and Induced Fit Pathways in Abiotic Hosts.
Radoslav Z PavlovićRemy F LalisseAlexandar L HansenChristopher A WaudbyZhiquan LeiMurat GüneyXiuze WangChristopher M HadadJovica D BadjićPublished in: Angewandte Chemie (International ed. in English) (2021)
Two limiting cases of molecular recognition, induced fit (IF) and conformational selection (CS), play a central role in allosteric regulation of natural systems. The IF paradigm states that a substrate "instructs" the host to change its shape after complexation, while CS asserts that a guest "selects" the optimal fit from an ensemble of preexisting host conformations. With no studies that quantitatively address the interplay of two limiting pathways in abiotic systems, we herein and for the first time describe the way by which twisted capsule M-1, encompassing two conformers M-1(+) and M-1(-), trap CX4 (X=Cl, Br) to give CX4 ⊂M-1(+) and CX4 ⊂M-1(-), with all four states being in thermal equilibrium. With the assistance of 2D EXSY, we found that CBr4 would, at its lower concentrations, bind M-1 via a M-1(+)→M-1(-)→CBr4 ⊂M-1(-) pathway corresponding to conformational selection. For M-1 complexing CCl4 though, data from 2D EXSY measurements and 1D NMR line-shape analysis suggested that lower CCl4 concentrations would favor CS while the IF pathway prevailed at higher proportions of the guest. Since CS and IF are not mutually exclusive, we reason that our work sets the stage for characterizing the dynamics of a wide range of already existing hosts to broaden our fundamental understanding of their action. The objective is to master the way in which encapsulation takes place for designing novel and allosteric sequestering agents, catalysts and chemosensors akin to those found in nature.
Keyphrases
- molecular dynamics
- molecular dynamics simulations
- single molecule
- high glucose
- diabetic rats
- small molecule
- drug induced
- liver injury
- magnetic resonance
- liver fibrosis
- high resolution
- endothelial cells
- deep learning
- highly efficient
- convolutional neural network
- artificial intelligence
- genome wide identification
- water soluble
- solid state
- case control
- big data
- data analysis