Relative Binding Free Energy Predictions for Inhibitors of Tetrameric Influenza Virus Neuraminidase

Accurate methods to predict the free energies of protein-ligand interactions have great potential to assist rational drug design. In this work, we used molecular dynamics simulations with alchemical perturbation to predict the binding of carbohydrate-based ligands to influenza virus neuraminidase (N2). This specific drug target is a challenging test system for alchemical free energy methods because it has flexible binding site motifs. We use a molecular dynamics protocol that works for longer time scales than are often reported in previous molecular dynamics studies of N2. We demonstrated that N2-ligand complex stability and that accurate N2 150-loop dynamics, on a 350 ns time scale, are both force field-dependent (AMBER99SB-ILDN, GAFF and TIP4P water are required). Further, we showed that crystallographic waters must be retained to maintain stability of N2-ligand complexes over 350 ns. Using our modelling protocol, we were able to determine relative binding free energy values between neuraminidase ligands which correlated strongly with experimental differences in pIC50 values (R = -0.96, ρ = 0.86, N = 13, sig < 0.0001). It is anticipated that the molecular dynamics protocol and the relative binding free energy methods reported here, will both be useful in expediting the discovery of novel therapeutics for N2 and other homologous glycohydrolases

A cyclic peptide inhibitor of the iNOS–SPSB protein–protein interaction as a potential anti-infective agent

SPRY domain- and SOCS box-containing proteins SPSB1, SPSB2, and SPSB4 interact with inducible nitric oxide synthase (iNOS), causing the iNOS to be polyubiquitinated and targeted for degradation. Inhibition of this interaction increases iNOS levels, and consequently cellular nitric oxide (NO) concentrations, and has been proposed as a potential strategy for killing intracellular pathogens. We previously described two DINNN-containing cyclic peptides (CP1 and CP2) as potent inhibitors of the murine SPSB-iNOS interaction. In this study, we report the crystal structures of human SPSB4 bound to CP1 and CP2 and human SPSB2 bound to CP2. We then used these structures to design a new inhibitor in which an intramolecular hydrogen bond was replaced with a hydrocarbon linkage to form a smaller macrocycle while maintaining the bound geometry of CP2 observed in the crystal structures. This resulting pentapeptide SPSB-iNOS inhibitor (CP3) has a reduced macrocycle ring size, fewer nonbinding residues, and includes additional conformational constraints. CP3 has a greater affinity for SBSB2 ( KD = 7 nM as determined by surface plasmon resonance) and strongly inhibits the SPSB2-iNOS interaction in macrophage cell lysates. We have also determined the crystal structure of CP3 in complex with human SPSB2, which reveals the structural basis for the increased potency of CP3 and validates the original design

Membrane Permeating Macrocycles: Design Guidelines from Machine Learning

The ability to predict cell-permeable candidate molecules has great potential to assist drug discovery projects. Large molecules that lie beyond the Rule of Five (bRo5) are increasingly important as drug candidates and tool molecules for chemical biology. However, such large molecules usually do not cross cell membranes and cannot access intracellular targets or be developed as orally bioavailable drugs. Here, we describe a random forest (RF) machine learning model for the prediction of passive membrane permeation rates developed using a set of over 1000 bRo5 macrocyclic compounds. The model is based on easily calculated chemical features/descriptors as independent variables. Our random forest (RF) model substantially outperforms a multiple linear regression model based on the same features and achieves better performance metrics than previously reported models using the same underlying data. These features include: (1) polar surface area in water, (2) the octanol-water partitioning coefficient, (3) the number of hydrogen-bond donors, (4) the sum of the topological distances between nitrogen atoms, (5) the sum of the topological distances between nitrogen and oxygen atoms, and (6) the multiple molecular path count of order 2. The last three features represent molecular flexibility, the ability of the molecule to adopt different conformations in the aqueous and membrane interior phases, and the molecular “chameleonicity.” Guided by the model, we propose design guidelines for membrane-permeating macrocycles. It is anticipated that this model will be useful in guiding the design of large, bioactive molecules for medicinal chemistry and chemical biology applications

Membrane Permeating Macrocycles: Design Guidelines from Machine Learning

The ability to predict cell-permeable candidate molecules has great potential to assist drug discovery projects. Large molecules that lie beyond the Rule of Five (bRo5) are increasingly important as drug candidates and tool molecules for chemical biology. However, such large molecules usually do not cross cell membranes and cannot access intracellular targets or be developed as orally bioavailable drugs. Here, we describe a random forest (RF) machine learning model for the prediction of passive membrane permeation rates developed using a set of over 1000 bRo5 macrocyclic compounds. The model is based on easily calculated chemical features/descriptors as independent variables. Our random forest (RF) model substantially outperforms a multiple linear regression model based on the same features and achieves better performance metrics than previously reported models using the same underlying data. These features include: (1) polar surface area in water, (2) the octanol-water partitioning coefficient, (3) the number of hydrogen-bond donors, (4) the sum of the topological distances between nitrogen atoms, (5) the sum of the topological distances between nitrogen and oxygen atoms, and (6) the multiple molecular path count of order 2. The last three features represent molecular flexibility, the ability of the molecule to adopt different conformations in the aqueous and membrane interior phases, and the molecular “chameleonicity.” Guided by the model, we propose design guidelines for membrane-permeating macrocycles. It is anticipated that this model will be useful in guiding the design of large, bioactive molecules for medicinal chemistry and chemical biology applications

Structural and functional characterisation of a novel peptide from the Australian sea anemone Actinia tenebrosa

Sea anemone venoms have long been recognised as a rich source of peptides with interesting pharmacological and structural properties. Our recent transcriptomic studies of the Australian sea anemone Actinia tenebrosa have identified a novel 13-residue peptide, U-AITx-Ate1. U-AITx-Ate1 contains a single disulfide bridge and bears no significant homology to previously reported amino acid sequences of peptides from sea anemones or other species. We have produced U-AITx-Ate1 using solid-phase peptide synthesis, followed by oxidative folding and purification of the folded peptide using reversed-phase high-performance liquid chromatography. The solution structure of U-AITx-Ate1 was determined based on two-dimensional nuclear magnetic resonance spectroscopic data. Diffusion-ordered NMR spectroscopy revealed that U-AITx-Ate1 was monomeric in solution. Perturbations in the 1D H NMR spectrum of U-AITx-Ate1 in the presence of dodecylphosphocholine micelles together with molecular dynamics simulations indicated an interaction of U-AITx-Ate1 with lipid membranes, although no binding was detected to 100% POPC and 80% POPC: 20% POPG lipid nanodiscs by isothermal titration calorimetry. Functional assays were performed to explore the biological activity profile of U-AITx-Ate1. U-AITx-Ate1 showed no activity in voltage-clamp electrophysiology assays and no change in behaviour and mortality rates in crustacea. Moderate cytotoxic activity was observed against two breast cancer cell lines

Fragment-Based Screening of a Natural Product Library against 62 Potential Malaria Drug Targets Employing Native Mass Spectrometry.

Natural products are well known for their biological relevance, high degree of three-dimensionality, and access to areas of largely unexplored chemical space. To shape our understanding of the interaction between natural products and protein targets in the postgenomic era, we have used native mass spectrometry to investigate 62 potential protein targets for malaria using a natural-product-based fragment library. We reveal here 96 low-molecular-weight natural products identified as binding partners of 32 of the putative malarial targets. Seventy-nine (79) fragments have direct growth inhibition on Plasmodium falciparum at concentrations that are promising for the development of fragment hits against these protein targets. This adds a fragment library to the published HTS active libraries in the public domain

Fragment-Based Screening of a Natural Product Library against 62 Potential Malaria Drug Targets Employing Native Mass Spectrometry

Natural products are well known for their biological relevance, high degree of three-dimensionality, and access to areas of largely unexplored chemical space. To shape our understanding of the interaction between natural products and protein targets in the postgenomic era, we have used native mass spectrometry to investigate 62 potential protein targets for malaria using a natural-product-based fragment library. We reveal here 96 low-molecular-weight natural products identified as binding partners of 32 of the putative malarial targets. Seventy-nine (79) fragments have direct growth inhibition on <i>Plasmodium falciparum</i> at concentrations that are promising for the development of fragment hits against these protein targets. This adds a fragment library to the published HTS active libraries in the public domain