Deconstructing Allostery: Computational Assessment of the Binding Determinants of Allosteric PTP1B Modulators



It is currently challenging to predict whether fragment hits that do not bind to an orthosteric site could be elaborated into allosteric modulators, as in these cases binding does not necessarily translate into a functional effect. We propose a workflow using Markov State Models (MSMs) with steered molecular dynamics (sMD) to assess the allosteric potential of known binders. sMD simulations are employed to sample protein conformational space inaccessible to routine equilibrium MD timescales. Protein conformations sampled by sMD provide starting points for seeded MD simulations, which are combined into MSMs. The methodology is demonstrated on a dataset of protein tyrosine phosphatase 1B ligands. Experimentally confirmed allosteric inhibitors are correctly classified as inhibitors, whereas the deconstructed analogues show reduced inhibitory activity. Analysis of the MSMs provide insights into preferred protein-ligand arrangements that correlate with functional outcomes. The present methodology may find applications for progressing fragments towards lead molecules in FBDD campaigns.


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Project AMMo
AMMo (Allostery in Markov Models) is a collection of a python library, command line scripts and tools, for assessing allosteric modulators.