Ligand Binding Swaps between Soft Internal Modes of α,β-Tubulin and Alters Its Accessible Conformational Space

The dynamic instability of the microtubule originates from the conformational switching of its building block, that is, the α, β-tubulin dimer. Ligands occupying the interface of the α–β dimer bias the switch toward the disintegration of the microtubule, which in turn controls the cell division. A little loop of tubulin is structurally encoded as a biophysical “gear” that works by changing its structural packing. The consequence of such change propagates to the quaternary level to alter the global dynamics and is reflected as a swapping between the relative contributions of dominating internal modes. Simulation shows that there is an appreciable separation between the conformational space accessed by the liganded and unliganded systems; the clusters of conformations differ in their intrinsic tendencies to “bend” and “twist”. The correlation between the altered breathing modes and conformational space rationally hypothesizes a mechanism of straight−bent interconversion of the system. In this mechanism, a ligand is understood to bias the state of the “gear” that detours the conformational equilibrium away from its native preference. Thus, a fundamental biophysical insight into the mechanism of the conformational switching of tubulin is presented as a multiscale process that also shows promise to yield newer concept of ligand design

Dynamic and Static Water Molecules Complement the TN16 Conformational Heterogeneity inside the Tubulin Cavity

TN16 is one of the most promising inhibitors of α, β dimer of tubulin that occupies the cavity in the β-subunit located at the dimeric interface, known as the colchicine binding site. The experimentally determined structure of the complex (Protein Data Bank entry 3HKD) presents the conformation and position of the ligand based on the “best fit”, keeping the controversy of other significant binding modes open for further investigation. Computation has already revealed that TN16 experiences fluctuations within the binding pocket, but the insight from that previous report was limited by the shorter windows of sampling and by the approximations on the surrounding environment by implicit solvation. This article reports that in most of the cases straightforward MMGBSA calculations of binding energy revealed a gradual loss of stabilization that was inconsistent with the structural observations, and thus, it indicated the lack of consideration of stabilizing factors with appropriate weightage. Consideration of the structurally packed water molecules in the space between the ligand and receptor successfully eliminated such discrepancies between the structure and stability, serving as the “litmus test” of the importance of explicit consideration of such structurally packed water in the calculations. Such consideration has further evidenced a quasi-degenerate character of the different binding modes of TN16 that has rationalized the observed intrinsic fluctuations of TN16 within the pocket, which is likely to be the most critical insight into its entropy-dominated binding. Quantum mechanical calculations have revealed a relay of electron density from TN16 to the protein via a water molecule in a concerted manner

Molecular Dynamics and Machine Learning Give Insights on the Flexibility–Activity Relationships in Tyrosine Kinome

Tyrosine kinases are a subfamily of kinases with critical roles in cellular machinery. Dysregulation of their active or inactive forms is associated with diseases like cancer. This study aimed to holistically understand their flexibility–activity relationships, focusing on pockets and fluctuations. We studied 43 different tyrosine kinases by collecting 120 μs of molecular dynamics simulations, pocket and residue fluctuation analysis, and a complementary machine learning approach. We found that the inactive forms often have increased flexibility, particularly at the DFG motif level. Noteworthy, thanks to these long simulations combined with a decision tree, we identified a semiquantitative fluctuation threshold of the DGF+3 residue over which the kinase has a higher probability to be in the inactive form

Molecular Dynamics and Machine Learning Give Insights on the Flexibility–Activity Relationships in Tyrosine Kinome

Tyrosine kinases are a subfamily of kinases with critical roles in cellular machinery. Dysregulation of their active or inactive forms is associated with diseases like cancer. This study aimed to holistically understand their flexibility–activity relationships, focusing on pockets and fluctuations. We studied 43 different tyrosine kinases by collecting 120 μs of molecular dynamics simulations, pocket and residue fluctuation analysis, and a complementary machine learning approach. We found that the inactive forms often have increased flexibility, particularly at the DFG motif level. Noteworthy, thanks to these long simulations combined with a decision tree, we identified a semiquantitative fluctuation threshold of the DGF+3 residue over which the kinase has a higher probability to be in the inactive form