Molecular Dynamics Simulations Establish the Molecular Basis for the Broad Allostery Hotspot Distributions in the Tetracycline Repressor

Abstract

It is imperative to identify the network of residues essential to the allosteric coupling for the purpose of rationally engineering allostery in proteins. Deep mutational scanning analysis has emerged as a function-centric approach for identifying such allostery hotspots in a comprehensive and unbiased fashion, leading to observations that challenge our understanding of allostery at the molecular level. Specifically, a recent deep mutational scanning study of the tetracycline repressor (TetR) revealed an unexpectedly broad distribution of allostery hotspots throughout the protein structure. Using extensive molecular dynamics simulations (up to 50 μs) and free energy computations, we establish the molecular and energetic basis for the strong anticooperativity between the ligand and DNA binding sites. The computed free energy landscapes in different ligation states illustrate that allostery in TetR is well described by a conformational selection model, in which the apo state samples a broad set of conformations, and specific ones are selectively stabilized by either ligand or DNA binding. By examining a range of structural and dynamic properties of residues at both local and global scales, we observe that various analyses capture different subsets of experimentally identified hotspots, suggesting that these residues modulate allostery in distinct ways. These results motivate the development of a thermodynamic model that qualitatively explains the broad distribution of hotspot residues and their distinct features in molecular dynamics simulations. The multifaceted strategy that we establish here for hotspot evaluations and our insights into their mechanistic contributions are useful for modulating protein allostery in mechanistic and engineering studies.

Piericones A and B as Potent Antithrombotics: Nanomolar Noncompetitive Protein Disulfide Isomerase Inhibitors with an Unexpected Chemical Architecture

“…To further confirm the binding mechanism, an atomistic molecular dynamics simulation on the complexes of PDI- 1 and - 2 was performed (Tables S6, S7 and Figures S31–S36). The conformations of PDI- 1 and - 2 complexes were both stable and showed no significant changes during a simulation cascade.…”

Deep mutational scanning and machine learning reveal structural and molecular rules governing allosteric hotspots in homologous proteins

“…An evolutionary perspective strengthens this hypothesis. The evolution of aTFs has occurred through a series of gene duplication events resulting in mixing and matching LBDs and DBDs ( Pougach et al, 2014 ; Yuan et al, 2022 ). Thus, the DBDs likely exist as standalone domains that respond to large thermodynamic changes (e.g., inducer binding).…”

Allosteric Communication in the Gating Mechanism for Controlled Protein Degradation by the Bacterial ClpP Peptidase

“…native as well as non-native, contacts. Nonlinear contributions to these couplings, quantified by considering the mutual information between residue coordinates 93 , have also been used to probe allosteric networks 58,60,61,[94][95][96][97] . We note that a recent study of the tetracycline repressor dimer identified similar trends in the inter-residue coupling when comparing linear and nonlinear contributions 97 .…”

“…Nonlinear contributions to these couplings, quantified by considering the mutual information between residue coordinates 93 , have also been used to probe allosteric networks 58,60,61,[94][95][96][97] . We note that a recent study of the tetracycline repressor dimer identified similar trends in the inter-residue coupling when comparing linear and nonlinear contributions 97 . A distinct advantage of networkbased approaches is their ability to probe the propagation of allosteric signal on timescales accessible to MD simulations, yet yielding results consistent with the much longer biological timescales 90,98 .…”