Accurately predicting receptor-ligand binding free energies is one of the holy grails of computational chemistry with many applications in chemistry and biology. Many successes have been reported, but issues relating to sampling and force field accuracy remain significant issues affecting our ability to reliably calculate binding free energies. In order to explore these issues in more detail we have examined a series of small host-guest complexes from the SAMPL6 blind challenge, namely octa-acids (OAs)-guest complexes and Curcurbit[8]uril (CB8)-guest complexes. Specifically, potential of mean force studies using umbrella sampling combined with the weighted histogram method were carried out on both systems with both known and unknown binding affinities. We find that using standard force fields and straightforward simulation protocols we are able to obtain satisfactory results, but that simply scaling our results allows us to significantly improve our predictive ability for the unknown test sets: the overall RMSD of the binding free energy versus experiment is reduced from 5.59 to 2.36 kcal/mol; for the CB8 test system, the RMSD goes from 8.04 to 3.51 kcal/mol, while for the OAs test system, the RSMD goes from 2.89 to 0.95 kcal/mol. The scaling approach was inspired by studies on structurally related known benchmark sets: by simply scaling, the RMSD was reduced from 6.23 to 1.19 kcal/mol and from 2.96 to 0.62 kcal/mol for the CB8 benchmark system and the OA benchmark system, respectively. We find this scaling procedure to correct absolute binding affinities to be highly effective especially when working across a "congeneric" series with similar charge states. It is less successful when applied to mixed ligands with varied charges and chemical characteristics, but improvement is still realized in the present case. This approach suggests that there are large systematic errors in absolute binding free energy calculations that can be straightforwardly accounted for using a scaling procedure. Random errors are still an issue, but near chemical accuracy can be obtained using the present strategy in select cases.
The rapid development of molecular structural databases provides the chemistry community access to an enormous array of experimental data that can be used to build and validate computational models. Using radial distribution functions collected from experimentally available X-ray and NMR structures, a number of so-called statistical potentials have been developed over the years using the structural data mining strategy. These potentials have been developed within the context of the two-particle Kirkwood equation by extending its original use for isotropic monatomic systems to anisotropic biomolecular systems. However, the accuracy and the unclear physical meaning of statistical potentials have long formed the central arguments against such methods. In this work, we present a new approach to generate molecular energy functions using structural data mining. Instead of employing the Kirkwood equation and introducing the "reference state" approximation, we model the multidimensional probability distributions of the molecular system using graphical models and generate the target pairwise Boltzmann probabilities using the Bayesian field theory. Different from the current statistical potentials that mimic the "knowledge-based" PMF based on the 2-particle Kirkwood equation, the graphical-model-based structure-derived potential developed in this study focuses on the generation of lower-dimensional Boltzmann distributions of atoms through reduction of dimensionality. We have named this new scoring function GARF, and in this work we focus on the mathematical derivation of our novel approach followed by validation studies on its ability to predict protein-ligand interactions.
Summary Pharmacological activation of the E3 ligase Parkin represents a rational therapeutic intervention for the treatment of Parkinson’s disease. Here we identify several compounds that enhance the activity of wildtype Parkin in the presence of phospho-ubiquitin and act as positive allosteric modulators (PAMs). While these compounds activate Parkin in a series of biochemical assays, they do not act by thermally destabilizing Parkin and fail to enhance the Parkin translocation rate to mitochondria or to enact mitophagy in cell-based assays. We conclude that in the context of the cellular milieu the therapeutic window to pharmacologically activate Parkin is very narrow.
Mixed Lineage Kinase domain-Like (MLKL), a key player in necroptosis, is a multi-domain protein with an N-terminal 4 helical bundle (4HB) and a pseudokinase domain (PsK) connected by brace helices. Phosphorylation of PsK domain of MLKL is a key step towards oligomerization of 4HB domain that causes cell death. Necrosulfonamide (NSA) binds to the 4HB domain of MLKL to inhibit necroptosis. To understand the molecular details of MLKL function and it’s inhibition, we have performed a molecular dynamic study on hMLKL protein in apo, phosphorylated and NSA-bound states for a total 3 μs simulation time. Our simulations show increased inter-domain flexibility, increased rigidification of the activation loop and increased alpha helical content in the brace helix region revealing a form of monomeric hMLKL necessary for oligomerization upon phosphorylation as compared to apo state. NSA binding disrupts this activated form and causes two main effects on hMLKL conformation: (1) locking of the relative orientation of 4HB and PsK domains by the formation of several new interactions and (2) prevention of key 4HB residues to participate in cross-linking for oligomer formation. This new understanding of the effect of hMLKL conformations on phosphorylation and NSA binding suggest new avenues for designing effective allosteric inhibitors of hMLKL.
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