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Updated: Jul 4, 2026

Structure-Guided Design and Development of Novel Cyclophilin A Inhibitors and Ganoderiol-F Derivatives: An In-Silico Approach
Published on: June 23, 2026
Design of protein-ligand binding based on the molecular-mechanics energy model
F Edward Boas1, Pehr B Harbury
1Department of Biochemistry, Stanford University School of Medicine, Beckman B437, 279 Campus Drive West, Stanford, CA 94305-5307, USA.
Abstract:
While the molecular-mechanics field has standardized on a few potential energy functions, computational protein design efforts are based on potentials that are unique to individual laboratories. Here we show that a standard molecular-mechanics potential energy function without any modifications can be used to engineer protein-ligand binding. A molecular-mechanics potential is used to reconstruct the coordinates of various binding sites with an average root-mean-square error of 0.61 A and to reproduce known ligand-induced side-chain conformational shifts. Within a series of 34 mutants, the calculation can always distinguish between weak (K(d)>1 mM) and tight (K(d)<10 microM) binding sequences. Starting from partial coordinates of the ribose-binding protein lacking the ligand and the 10 primary contact residues, the molecular-mechanics potential is used to redesign a ribose-binding site. Out of a search space of 2 x 10(12) sequences, the calculation selects a point mutant of the native protein as the top solution (experimental K(d)=17 microM) and the native protein as the second best solution (experimental K(d)=210 nM). The quality of the predictions depends on the accuracy of the generalized Born electrostatics model, treatment of protonation equilibria, high-resolution rotamer sampling, a final local energy minimization step, and explicit modeling of the bound, unbound, and unfolded states. The application of unmodified molecular-mechanics potentials to protein design links two fields in a mutually beneficial way. Design provides a new avenue for testing molecular-mechanics energy functions, and future improvements in these energy functions will presumably lead to more accurate design results.
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