Anna M. Perna

Identification of E-cadherin signature motifs functioning as cleavage sites for Helicobacter pylori HtrA

The cell adhesion protein and tumour suppressor E-cadherin exhibits important functions in the prevention of gastric cancer. As a class-I carcinogen, Helicobacter pylori (H. pylori) has developed a unique strategy to interfere with E-cadherin functions. In previous studies, we have demonstrated that H. pylori secretes the protease high temperature requirement A (HtrA) which cleaves off the E-cadherin ectodomain (NTF) on epithelial cells. This opens cell-to-cell junctions, allowing bacterial transmigration across the polarised epithelium. Here, we investigated the molecular mechanism of the HtrA-E-cadherin interaction and identified E-cadherin cleavage sites for HtrA. Mass-spectrometry-based proteomics and Edman degradation revealed three signature motifs containing the [VITA]-[VITA]-x-x-D-[DN] sequence pattern, which were preferentially cleaved by HtrA. Based on these sites, we developed a substrate-derived peptide inhibitor that selectively bound and inhibited HtrA, thereby blocking transmigration of H. pylori. The discovery of HtrA-targeted signature sites might further explain why we detected a stable 90 kDa NTF fragment during H. pylori infection, but also additional E-cadherin fragments ranging from 105 kDa to 48 kDa in in vitro cleavage experiments. In conclusion, HtrA targets E-cadherin signature sites that are accessible in in vitro reactions, but might be partially masked on epithelial cells through functional homophilic E-cadherin interactions.

Scrutinizing MHC-I Binding Peptides and Their Limits of Variation

Designed peptides that bind to major histocompatibility protein I (MHC-I) allomorphs bear the promise of representing epitopes that stimulate a desired immune response. A rigorous bioinformatical exploration of sequence patterns hidden in peptides that bind to the mouse MHC-I allomorph H-2Kb is presented. We exemplify and validate these motif findings by systematically dissecting the epitope SIINFEKL and analyzing the resulting fragments for their binding potential to H-2Kb in a thermal denaturation assay. The results demonstrate that only fragments exclusively retaining the carboxy- or amino-terminus of the reference peptide exhibit significant binding potential, with the N-terminal pentapeptide SIINF as shortest ligand. This study demonstrates that sophisticated machine-learning algorithms excel at extracting fine-grained patterns from peptide sequence data and predicting MHC-I binding peptides, thereby considerably extending existing linear prediction models and providing a fresh view on the computer-based molecular design of future synthetic vaccines. The server for prediction is available at http://modlab-cadd.ethz.ch (SLiDER tool, MHC-I version 2012).

Combinatorial chemistry by ant colony optimization

BACKGROUND Prioritizing building blocks for combinatorial medicinal chemistry represents an optimization task. We present the application of an artificial ant colony algorithm to combinatorial molecular design (Molecular Ant Algorithm [MAntA]). RESULTS In a retrospective evaluation, the ant algorithm performed favorably compared with other stochastic optimization methods. Application of MAntA to peptide design resulted in new octapeptides exhibiting substantial binding to mouse MHC-I (H-2K(b)). In a second study, MAntA generated a new functional factor Xa inhibitor by Ugi-type three-component reaction. CONCLUSION This proof-of-concept study validates artificial ant systems as innovative computational tools for efficient building block prioritization in combinatorial chemistry. Focused activity-enriched compound collections are obtained without the need for exhaustive product enumeration.

Virtual screening for compounds that mimic protein–protein interface epitopes

Modulation of protein–protein interactions (PPI) has emerged as a new concept in rational drug design. Here, we present a computational protocol for identifying potential PPI inhibitors. Relevant regions of interfaces (epitopes) are predicted for three‐dimensional protein models and serve as queries for virtual compound screening. We present a computational screening protocol that incorporates two different pharmacophore models. One model is based on the mathematical concept of autocorrelation vectors and the other utilizes fuzzy labeled graphs. In a proof‐of‐concept study, we were able to identify serine protease inhibitors using a predicted trypsin epitope as query. Our virtual screening framework may be suited for rapid identification of PPI inhibitors and suggesting bioactive tool compounds. Copyright for JCC Journal:

Fragment-Based De Novo Design Reveals a Small-Molecule Inhibitor of Helicobacter Pylori HtrA†

Sustained identification of innovative chemical entities is key for the success of chemical biology and drug discovery. We report the fragment-based, computer-assisted de novo design of a small molecule inhibiting Helicobacter pylori HtrA protease. Molecular binding of the designed compound to HtrA was confirmed through biophysical methods, supporting its functional activity in vitro. Hit expansion led to the identification of the currently best-in-class HtrA inhibitor. The results obtained reinforce the validity of ligand-based de novo design and binding-kinetics-guided optimization for the efficient discovery of pioneering lead structures and prototyping drug-like chemical probes with tailored bioactivity.

Adaptive peptide design.

Computer algorithms help in the identification and optimization of peptides with desired structure and function. We provide an overview of the current focus of our research group in this field, highlighting innovative methods for peptide representation and de novo peptide generation. Our evolutionary molecular design cycle contains structure-activity relationship modeling by machine-learning methods, virtual peptide generation, activity prediction, peptide syntheses, as well as biophysical and biochemical activity determination. Such interplay between computer-assisted peptide generation and scoring with real laboratory experiments enables rapid feedback throughout the design cycle so that adaptive optimization can take place. Selected practical applications are reviewed including the design of new immunomodulatory MHC-I binding peptides and antimicrobial peptides.

Peptide lineup against Gram-negative bacterial infection – first-in-class peptide inhibitor of H. pylori HtrA

More than 50% of the world population is infected with Helicobacter pylori (H. pylori) and the actual H. pylori treatments fail with increased regularity because of continuously rising antibiotic resistances. To meet this challenge, we focus on the development of a new antiinfective therapy against H. pylori by targeting a secreted enzyme, high temperature requirement A (HtrA). Release of the serine protease HtrA near the host’s gastric epithelial cells leads to loss of cellular adhesion due to E- cadherin cleavage [1]. We investigated the substrate cleavage sites of HtrA in its natural substrate E-cadherin performing a label-free mass spectrometrybased proteomic analysis and identified preferred cleavage positions by Edman sequencing. Further, we developed the first HtrA peptide inhibitor by synthesizing cleavage site fragments and analogues. Surface plasmon resonance (SPR) was used to perform binding studies. In vitro substrate cleavage assays as well as cellular infection assays fully support the biophysical data.