To obtain information about the functional importance of amino acids required for effective erythropoietin (EPO) mimetic action, the conserved residues of a peptide mimetic of EPO, recently discovered by phage display, were subjected to an alanine replacement strategy. Further, to identify a minimal mimetic peptide sequence, a series of truncation peptides has been generated. One EPO mimetic peptide sequence, EMP1, was targeted and more than 25 derivatives of this sequence were evaluated for their ability to compete with [125I]EPO for receptor binding and for their ability to support the proliferation of two EPO-responsive cell lines. Two hydrophobic amino acids, Tyr4 and Trp13, appear essential for mimetic action, and aromatic residues appear to be important at these sites. These findings are consistent with the previously reported X-ray crystal structure of EMP1 complexed with the extracellular domain of the EPO receptor (EPO binding protein; EBP). In our efforts to define the structural elements required for EPO mimetic action, a 13 amino acid peptide was identified which possesses mimetic properties and contains a minimal agonist epitope. The ability of this peptide to effectively serve as a mimetic capable of the induction of EPO-responsive cell proliferation appears to reside within a single residue, equivalent to position Tyr4 of EMP1, when present in a sequence that includes the cyclic core peptide structure. Although these peptides are less potent than EPO, they should serve as an excellent starting point for the design of compounds with EPO mimetic activity.
In May and August, 2016, several pharmaceutical companies convened to discuss and compare experiences with Free Energy Perturbation (FEP). This unusual synchronization of interest was prompted by Schrödinger's FEP+ implementation and offered the opportunity to share fresh studies with FEP and enable broader discussions on the topic. This article summarizes key conclusions of the meetings, including a path forward of actions for this group to aid the accelerated evaluation, application and development of free energy and related quantitative, structure-based design methods.
A series of 48 steroids has been studied with the SYBYL QSAR module using Relative Binding Affinities (RBAs) to progesterone and androgen receptors obtained from the literature. Models for the progesterone and androgen data were developed. Both models show regions where sterics and electrostatics correlate to binding affinity but are different for androgen and progesterone which suggests differences possibly important for receptor selectivity. The progesterone model is more predictive than the androgen (predictive r2 of 0.725 vs. 0.545 for progesterone and androgen, respectively).
Accurate prediction of intermolecular interaction energies is a fundamental challenge in electronic structure theory due to their subtle character and small magnitudes relative to total molecular energies. Symmetry adapted perturbation theory (SAPT) provides rigorous quantum mechanical means for computing such quantities directly and accurately, but for a computational cost of at least O(N5), where N is the number of atoms. Here, we report machine learned models of SAPT components with a computational cost that scales asymptotically linearly, O(N). We use modified multi-target Behler–Parrinello neural networks and specialized intermolecular symmetry functions to address the idiosyncrasies of the intermolecular problem, achieving 1.2 kcal mol−1 mean absolute errors on a test set of hydrogen bound complexes including structural data extracted from the Cambridge Structural Database and Protein Data Bank, spanning an interaction energy range of 20 kcal mol−1. Additionally, we recover accurate predictions of the physically meaningful SAPT component energies, of which dispersion and induction/polarization were the easiest to predict and electrostatics and exchange–repulsion are the most difficult.
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