Configurational Entropy in Protein–Peptide Binding:

Abstract

Configurational entropy is thought to influence biomolecular processes, but there are still many open questions about this quantity, including its magnitude, its relationship to molecular structure, and the importance of correlation. The mutual information expansion (MIE) provides a novel and systematic approach to computing configurational entropy changes due to correlated motions from molecular simulations. Here, we present the first application of the MIE method to protein-ligand binding, using multiple molecular dynamics simulations (MMDSs) to study association of the UEV domain of the protein Tsg101 and an HIV-derived nonapeptide. The current investigation utilizes the second-order MIE approximation, which treats correlations between all pairs of degrees of freedom. The computed change in configurational entropy is large and is found to have a major contribution from changes in pairwise correlation. The results also reveal intricate structure-entropy relationships. Thus, the present analysis suggests that, in order for a model of binding to be accurate, it must include a careful accounting of configurational entropy changes.

Bak Conformational Changes Induced by Ligand Binding: Insight into BH3 Domain Binding and Bak Homo-Oligomerization

“…To confirm this, we calculated intermolecular interaction energies of the Noxa·Bak complex and a reference complex of A1234567891011121314151617181920212223242526K27·Bak that was generated by mutating residues 19–45 of Noxa in the Noxa·Bak complex model to A1920212223242526272829303132333435363738394041424344K45. The mutation to A1920212223242526272829303132333435363738394041424344K45 rather than A192021222324252627282930313233343536<...>…”

“…The initial structure of m Noxa· m Mcl-1 or m Puma· m Mcl-1 used for the simulations was taken from the first NMR model of Protein Data Bank code of 2ROD or 2ROC, respectively. The initial structure of A1234567891011121314151617181920212223242526K27·Bak was obtained from mutation from the simulation-refined Noxa·Bak model. b CRMSD: alpha carbon root mean square deviation between the average structure of 5,100 similar conformers identified by cluster analysis from the second-round simulation of Noxa·Bak and the energy-minimized average structure of 271 similar conformers identified by cluster analysis from the first-round simulation of Noxa·Bak, where the average structure of the 5,100 conformers was not subjected to any energy minimization. c CRMSD: alpha carbon root mean square deviation between the average structure of 2,000 similar conformers identified by cluster analysis from the simulations of A1234567891011121314151617181920212223242526K27·Bak and the energy-minimized structure of A1234567891011121314151617181920212223242526K…”

Thermostabilization of the β1-Adrenergic Receptor Correlates with Increased Entropy of the Inactive State

“…To analyze if these residues are involved in activation leading to concerted motion between the ligand binding site and G-protein coupling site, we have analyzed the correlated movement of residue pairs during the dynamics of the wt-β 1 AR and m23-β 1 AR, using Mutual Information 33–35 . Correlation in movement between residues that are far removed in sequence leads to a reduction in entropy, because it reduces the number of unique conformational states sampled.…”

“…Correlation in movement between residues that are far removed in sequence leads to a reduction in entropy, because it reduces the number of unique conformational states sampled. Mutual Information has been used previously for the identification of allosteric communication pathways 34,44 and the quantification of conformational entropy 33 .…”

The Semiempirical Quantum Mechanical Scoring Function for In Silico Drug Design

“…For proteins, configurational/conformational entropies can be extracted from MD simulations and/or obtained using information theory but at a high computational cost. [104,105] As mentioned above, we have reached the stage when hundreds of P-L complex configurations sampled by MD can be rescored by a QM-based scoring function with only a modest increase in computational time. [66] The continuous work on QM-enhanced docking and scoring in our laboratory has led to a fast, cheap and efficient protocol for screening large (in the order of 10 3 -10 6 entries) databases of compounds.…”