AI Insight
Researchers developed a new computational method called internal coordinate PC-entropy (iPC-entropy) that uses molecular dynamics simulations and machine learning to quantify how molecular flexibility contributes to entropy in drug-ligand binding. The study found that the major entropic penalty when drugs bind to proteins comes from the restriction of specific rotational movements (dihedrals) in the drug molecule itself, rather than from protein reorganization, and revealed previously unknown coupling between angle bending and torsional rotation that increases with molecular size. The method accurately reproduced experimental entropy measurements and identified which specific molecular motions are most important for binding thermodynamics.
Why it matters
This work provides a quantitative framework for understanding why some drug candidates bind less favorably than expected due to loss of molecular flexibility. The findings could improve computer-aided drug design by enabling more accurate predictions of binding strength and guiding chemists to design molecules that minimize unfavorable entropic penalties.
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⚠️ Preprint – Noch nicht peer-reviewed
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Molecular flexibility governs how molecules behave, reorganize, and respond to their environment. Although experiments measure molar entropy for small molecules and molecular dynamics (MD) simulations capture molecular motions, quantifying configuration entropy and the concerted internal motions such as torsion rotations, angle bending, and their couplings are central to understanding thermodynamic behavior but remains challenging. To dissect these contributions, we used MD trajectories and developed an internal coordinate PC-entropy (iPC-entropy) method to probe the origins of entropy and reveal how specific motions shape the thermodynamic landscape. The studies accurately captured molar entropy, identified key torsional motions as major contributors, and uncovered a critical angle-torsion coupling in which angle bending was strongly correlated with torsional rotation, a coupling that increases nonlinearly with molecular size. Evaluating entropic changes upon protein-ligand binding reveals that dominant entropic penalty arises from ligand dihedral rigidification rather than protein reorganization and highlights the specific dihedral rotations that become restricted. We also suggest systematic corrections for approaches considering solely rotamers to reliably reproduce the relative entropic penalty in computer-aided drug discovery. Together, our findings elucidate the molecular origins of entropy and entropy changes. In addition, we can quantify and illustrate the internal motions that strongly shape binding thermodynamics, thereby offering mechanistic insights to guide drug development.