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Understanding how mutations render a drug ineffective is a problem of immense relevance. Often the mechanism through which mutations cause drug resistance can be explained purely through thermodynamics. However, the more perplexing situation is when two proteins have the same drug binding affinities but different residence times. In this work, we demonstrate how all‐atom molecular dynamics simulations using recent developments grounded in statistical mechanics can provide a detailed mechanistic rationale for such variances. We discover dissociation mechanisms for the anti‐cancer drug Imatinib (Gleevec) against wild‐type and the N368S mutant of Abl kinase. We show how this point mutation triggers far‐reaching changes in the protein's flexibility and leads to a different, much faster, drug dissociation pathway. We believe that this work marks an efficient and scalable approach to obtain mechanistic insight into resistance mutations in biomolecular receptors that are hard to explain using a structural perspective. [ABSTRACT FROM AUTHOR]

Monte Carlo simulations and analytical results are used to demonstrate that the hexagonal close-packed (h.c.p.) pair correlation functions for different values of randomly distributed growth and deformation faults can be collapsed into master curves when plotted against a spatial variable, scaled with respect to a characteristic length ( L). The functional dependences of L on different types of faults are found to be non-universal. A simple method for the determination of L from the measured intensity distributions is also outlined. [ABSTRACT FROM AUTHOR]

An analytical expression for the characteristic length scale (L) of the pair correlation functions for randomly faulted hexagonal close-packed structures is derived for the case when the roots of the characteristic equation are all real. The values of L obtained by this analytical treatment are in good agreement with those obtained numerically. [ABSTRACT FROM AUTHOR]