All-Atom Model for Stabilization of α-Helical Structure in Peptides by Hydrocarbon Staples

Recent work has shown that the incorporation of an all-hydrocarbon “staple” into peptides can greatly increase their α-helix propensity, leading to an improvement in pharmaceutical properties such as proteolytic stability, receptor affinity and cell-permeability. Stapled peptides thus show promise as a new class of drugs capable of accessing intractable targets such as those that engage in intracellular protein-protein interactions. The extent of α-helix stabilization provided by stapling has proven to be substantially context dependent, requiring cumbersome screening to identify the optimal site for staple incorporation. In certain cases, a staple encompassing one turn of the helix (attached at residues i and i+4) furnishes greater helix stabilization than one encompassing two turns (i,i+7 staple), which runs counter to expectation based on polymer theory. These findings highlight the need for a more thorough understanding of the forces that underlie helix stabilization by hydrocarbon staples. Here we report all-atom Monte Carlo folding simulations comparing unmodified peptides derived from RNAse A and BID BH3 with various i,i+4 and i,i+7 stapled versions thereof. The results of these simulations were found to be in quantitative agreement with experimentally determined helix propensities. We also discovered that staples can stabilize quasi-stable decoy conformations, and that the removal of these states plays a major role in determining the helix stability of stapled peptides. Finally, we critically investigate why our method works, exposing the underlying physical forces that stabilize stapled peptides.Chemistry and Chemical Biolog

Iterative focused screening with biological fingerprints identifies selective Asc-1 inhibitors distinct from traditional high throughput screening

N-methyl-d-aspartate receptors (NMDARs) mediate glutamatergic signaling that is critical to cognitive processes in the central nervous system, and NMDAR hypofunction is thought to contribute to cognitive impairment observed in both schizophrenia and Alzheimer’s disease. One approach to enhance the function of NMDAR is to increase the concentration of an NMDAR coagonist, such as glycine or d-serine, in the synaptic cleft. Inhibition of alanine–serine–cysteine transporter-1 (Asc-1), the primary transporter of d-serine, is attractive because the transporter is localized to neurons in brain regions critical to cognitive function, including the hippocampus and cortical layers III and IV, and is colocalized with d-serine and NMDARs. To identify novel Asc-1 inhibitors, two different screening approaches were performed with whole-cell amino acid uptake in heterologous cells stably expressing human Asc-1: (1) a high-throughput screen (HTS) of 3 M compounds measuring 35S l-cysteine uptake into cells attached to scintillation proximity assay beads in a 1536 well format and (2) an iterative focused screen (IFS) of a 45 000 compound diversity set using a 3H d-serine uptake assay with a liquid scintillation plate reader in a 384 well format. Critically important for both screening approaches was the implementation of counter screens to remove nonspecific inhibitors of radioactive amino acid uptake. Furthermore, a 15 000 compound expansion step incorporating both on- and off-target data into chemical and biological fingerprint-based models for selection of additional hits enabled the identification of novel Asc-1-selective chemical matter from the IFS that was not identified in the full-collection HTS

The use of 2D fingerprint methods to support the assessment of structural similarity in orphan drug legislation.

In the European Union, medicines are authorised for some rare disease only if they are judged to be dissimilar to authorised orphan drugs for that disease. This paper describes the use of 2D fingerprints to show the extent of the relationship between computed levels of structural similarity for pairs of molecules and expert judgments of the similarities of those pairs. The resulting relationship can be used to provide input to the assessment of new active compounds for which orphan drug authorisation is being sought

Pocket Crafter: A 3D Generative Modeling Based Workflow for the Rapid Generation of Hit Molecules in Drug Discovery

We present a user-friendly molecular generative pipeline called Pocket Crafter, specifically designed to facilitate hit finding activity in the drug discovery process. This workflow utilized a 3D (three-dimensional) generative modeling method, e.g. Pocket2Mol, for the de novo design of molecules in spatial perspective for the targeted protein structures, followed by filters for chemical-physical properties and drug-likeness, SAR (structure-activity relationship) analysis, and clustering to generate top virtual hit scaffolds. In our WDR5 case study, we acquired a focused set of 2029 compounds after a targeted searching within Novartis archived library based on the virtual scaffolds. Subsequently, we experimentally profiled these compounds, resulting in a novel chemical scaffold series that demonstrated activity in biochemical and biophysical assays. Pocket Crafter successfully prototyped an effective end-to-end 3D generative chemistry-based workflow for the exploration of new chemical scaffolds, which represents a promising approach in early drug discovery for the identification of novel active compounds

Structure of the stapled p53 peptide bound to Mdm2

Mdm2 is a major negative regulator of the tumor suppressor p53 protein, a protein that plays a crucial role in maintaining genome integrity. Inactivation of p53 is the most prevalent defect in human cancers. Inhibitors of the Mdm2 − p53 interaction that restore the functional p53 constitute potential nongenotoxic anticancer agents with a novel mode of action. We present here a 2.0 Å resolution structure of the Mdm2 protein with a bound stapled p53 peptide. Such peptides, which are conformationally and proteolytically stabilized with all-hydrocarbon staples, are an emerging class of biologics that are capable of disrupting protein − protein interactions and thus have broad therapeutic potential. The structure represents the first crystal structure of an i , i + 7 stapled peptide bound to its target and reveals that rather than acting solely as a passive conformational brace, a staple can intimately interact with the surface of a protein and augment the binding interface

Representing high throughput expression profiles via perturbation barcodes reveals compound targets

<div><p>High throughput mRNA expression profiling can be used to characterize the response of cell culture models to perturbations such as pharmacologic modulators and genetic perturbations. As profiling campaigns expand in scope, it is important to homogenize, summarize, and analyze the resulting data in a manner that captures significant biological signals in spite of various noise sources such as batch effects and stochastic variation. We used the L1000 platform for large-scale profiling of 978 representative genes across thousands of compound treatments. Here, a method is described that uses deep learning techniques to convert the expression changes of the landmark genes into a perturbation barcode that reveals important features of the underlying data, performing better than the raw data in revealing important biological insights. The barcode captures compound structure and target information, and predicts a compound’s high throughput screening promiscuity, to a higher degree than the original data measurements, indicating that the approach uncovers underlying factors of the expression data that are otherwise entangled or masked by noise. Furthermore, we demonstrate that visualizations derived from the perturbation barcode can be used to more sensitively assign functions to unknown compounds through a guilt-by-association approach, which we use to predict and experimentally validate the activity of compounds on the MAPK pathway. The demonstrated application of deep metric learning to large-scale chemical genetics projects highlights the utility of this and related approaches to the extraction of insights and testable hypotheses from big, sometimes noisy data.</p></div

Visualizations of the data based on z-scores or perturbation barcodes were examined to select candidate compounds in the phenotypic neighborhood of a series of known MAPK pathway inhibitors.

<p><b>(A–D)</b> t-SNE maps of the data, z-scores on top, perturbation barcode maps on the bottom. <b>(A, B)</b> the entire dataset is shown with the tested compounds in dark blue. <b>(C,D)</b> The neighborhood of the query MAPK pathway inhibitor compounds (orange) is shown. Common MAPK tools used for nearest neighbor analysis are circled. <b>(E,F)</b> Results of AP-1 reporter assays. Known MAPK actives are distinguished from unknowns predicted to be active in (C,D). <b>(G,H)</b> Rather than selecting neighbors of seed MAPK tool compounds in the t-SNE map, nearest neighbors in the native datasets were selected and tested in the AP-1 reporter assay. Key as in (E,F). See Fig C in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005335#pcbi.1005335.s001" target="_blank">S1 Text</a> for breakdown by categories, including overlaps.</p

Experimental setup and architecture of the deep model used.

<p><b>(A)</b> Cells treated with compounds in 384-well plates. <b>(B)</b> Cell lysate used for ligation mediated PCR with gene-specific probe pairs, and the gene expression measured using an optically addressed bead array technology. <b>(C)</b> Raw intensity is normalized and converted to relative expression changes versus control (z-scores) on a plate-wise basis. Variability is observed between biological replicates.</p

Performance of perturbation barcodes on public LINCS data.

<p>Analyses correspond to Rows 1–3 of <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005335#pcbi.1005335.t001" target="_blank">Table 1</a>.</p

Structure of the Stapled p53 Peptide Bound to Mdm2

Mdm2 is a major negative regulator of the tumor suppressor p53 protein, a protein that plays a crucial role in maintaining genome integrity. Inactivation of p53 is the most prevalent defect in human cancers. Inhibitors of the Mdm2–p53 interaction that restore the functional p53 constitute potential nongenotoxic anticancer agents with a novel mode of action. We present here a 2.0 Å resolution structure of the Mdm2 protein with a bound stapled p53 peptide. Such peptides, which are conformationally and proteolytically stabilized with all-hydrocarbon staples, are an emerging class of biologics that are capable of disrupting protein–protein interactions and thus have broad therapeutic potential. The structure represents the first crystal structure of an <i>i</i>, <i>i</i> + 7 stapled peptide bound to its target and reveals that rather than acting solely as a passive conformational brace, a staple can intimately interact with the surface of a protein and augment the binding interface