Drug bioactivity is conventionally viewed to be proportional to its binding affinity for a protein’s primary active site. However, allosteric drugs target remote sites from which a series of short-range interactions influence the primary site. Targeting allosteric modulation is of increasing importance in drug discovery due to advantages such as increased specificity for the target protein and tunability of the effect on protein functionality. Experimental and computational workflows incorporating high-throughput screening and machine learning are well-established in the development of traditional, orthosteric drugs. The success of these methodologies is primarily due to the simplicity of orthosteric drug action. However, allosteric drug action involves two active sites and interactions between them; defining and identifying the pathways of interactions between active sites is a significant hindrance to the development of an analogous approach for the rational design of allosteric drugs. Here we present Linear Response-Allosteric Pathway Analysis (LR-APA), a method to identify allosteric paths from all atom Molecular Dynamics (aaMD) simulations. LR-APA utilizes the effective harmonic Hessian as a weighted graph and identifies paths in this graph that yield the positional covariance matrix (PCM) of two sites. We then show how these paths can be used to reconstruct the PCM in elastic network models and apply LR-APA to aaMD trajectories of the IGPS protein, a benchmark allosteric system. By elucidating the physics contributing to the allosteric mechanism this formal perspective on the allosteric paths supports the development of an in silico allosteric drug design workflow.

Identifying and quantifying allosteric pathways from molecular simulations

2019 Fall National Meeting

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