Max Pillong

Aminothiazole-Featured Pirinixic Acid Derivatives As Dual 5-Lipoxygenase and Microsomal Prostaglandin E2 Synthase-1 Inhibitors with Improved Potency and Efficiency in Vivo

Dual inhibition of microsomal prostaglandin E2 synthase-1 (mPGES-1) and 5-lipoxygenase (5-LO) is currently pursued as potential pharmacological strategy for treatment of inflammation and cancer. Here we present a series of 26 novel 2-aminothiazole-featured pirinixic acid derivatives as dual 5-LO/mPGES-1 inhibitors with improved potency (exemplified by compound 16 (2-[(4-chloro-6-{[4-(naphthalen-2-yl)-1,3-thiazol-2-yl]amino}pyrimidin-2-yl)sulfanyl]octanoic acid) with IC50 = 0.3 and 0.4 μM, respectively) and bioactivity in vivo. Computational analysis presumes binding sites of 16 at the tip of the 5-LO catalytic domain and within a subpocket of the mPGES-1 active site. Compound 16 (10 μM) hardly suppressed cyclooxygenase (COX)-1/2 activities, failed to inhibit 12/15-LOs, and is devoid of radical scavenger properties. Finally, compound 16 reduced vascular permeability and inflammatory cell infiltration in a zymosan-induced mouse peritonitis model accompanied by impaired levels of cysteinyl-leukotrienes and prostaglandin E2. Together, 2-aminothiazole-featured pirinixic acids represent potent dual 5-LO/mPGES-1 inhibitors with an attractive pharmacological profile as anti-inflammatory drugs.

Deorphaning Pyrrolopyrazines as Potent Multi‐Target Antimalarial Agents

The discovery of pyrrolopyrazines as potent antimalarial agents is presented, with the most effective compounds exhibiting EC50 values in the low nanomolar range against asexual blood stages of Plasmodium falciparum in human red blood cells, and Plasmodium berghei liver schizonts, with negligible HepG2 cytotoxicity. Their potential mode of action is uncovered by predicting macromolecular targets through avant-garde computer modeling. The consensus prediction method suggested a functional resemblance between ligand binding sites in non-homologous target proteins, linking the observed parasite elimination to IspD, an enzyme from the non-mevalonate pathway of isoprenoid biosynthesis, and multi-kinase inhibition. Further computational analysis suggested essential P. falciparum kinases as likely targets of our lead compound. The results obtained validate our methodology for ligand- and structure-based target prediction, expand the bioinformatics toolbox for proteome mining, and provide unique access to deciphering polypharmacological effects of bioactive chemical agents.

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).

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:

Piloting the Membranolytic Activities of Peptides with a Self-organizing Map

Antimicrobial peptides (AMPs) show remarkable selectivity toward lipid membranes and possess promising antibiotic potential. Their modes of action are diverse and not fully understood, and innovative peptide design strategies are needed to generate AMPs with improved properties. We present a de novo peptide design approach that resulted in new AMPs possessing low‐nanomolar membranolytic activities. Thermal analysis revealed an entropy‐driven mechanism of action. The study demonstrates sustained potential of advanced computational methods for designing peptides with the desired activity.

Computer-assisted quantification of motile and invasive capabilities of cancer cells

High-throughput analysis of cancer cell dissemination and its control by extrinsic and intrinsic cellular factors is hampered by the lack of adequate and efficient analytical tools for quantifying cell motility. Oncology research would greatly benefit from such a methodology that allows to rapidly determine the motile behaviour of cancer cells under different environmental conditions, including inside three-dimensional matrices. We combined automated microscopy imaging of two- and three-dimensional cell cultures with computational image analysis into a single assay platform for studying cell dissemination in high-throughput. We have validated this new approach for medulloblastoma, a metastatic paediatric brain tumour, in combination with the activation of growth factor signalling pathways with established pro-migratory functions. The platform enabled the detection of primary tumour and patient-derived xenograft cell sensitivity to growth factor-dependent motility and dissemination and identified tumour subgroup-specific responses to selected growth factors of excellent diagnostic value.

Boswellic acids target the human immune system-modulating antimicrobial peptide LL-37.

The antimicrobial peptide LL-37 is the sole member of the human cathelicidin family with immune system-modulating properties and roles in autoimmune disease development. Small molecules able to interact with LL-37 and to modulate its functions have not been described yet. Boswellic acids (BAs) are pentacyclic triterpene acids that are bioactive principles of frankincense extracts used as anti-inflammatory remedies. Although various anti-inflammatory modes of action have been proposed for BAs, the pharmacological profile of these compounds is still incompletely understood. Here, we describe the identification of human LL-37 as functional target of BAs. In unbiased target fishing experiments using immobilized BAs as bait and human neutrophils as target source, LL-37 was identified as binding partner assisted by MALDI-TOF mass spectrometry. Thermal stability experiments using circular dichroism spectroscopy confirm direct interaction between BAs and LL-37. Of interest, this binding of BAs resulted in an inhibition of the functionality of LL-37. Thus, the LPS-neutralizing properties of isolated LL-37 were inhibited by 3-O-acetyl-β-BA (Aβ-BA) and 3-O-acetyl-11-keto-β-BA (AKβ-BA) in a cell-free limulus amoebocyte lysate assay with EC50=0.2 and 0.8 μM, respectively. Also, LL-37 activity was inhibited by these BAs in LL-37-enriched supernatants of stimulated neutrophils or human plasma derived from stimulated human whole blood. Together, we reveal BAs as inhibitors of LL-37, which might be a relevant mechanism underlying the anti-inflammatory properties of BAs and suggests BAs as suitable chemical tools or potential agents for intervention with LL-37 and related disorders.

Rational Design of Membrane-Pore-Forming Peptides

Specific interactions of peptides with lipid membranes are essential for cellular communication and constitute a central aspect of the innate host defense against pathogens. A computational method for generating innovative membrane-pore-forming peptides inspired by natural templates is presented. Peptide representation in terms of sequence- and topology-dependent hydrophobic moments is introduced. This design concept proves to be appropriate for the de novo generation of first-in-class membrane-active peptides with the anticipated mode of action. The designed peptides outperform the natural template in terms of their antibacterial activity. They form a kinked helical structure and self-assemble in the membrane by an entropy-driven mechanism to form dynamically growing pores that are dependent on the lipid composition. The results of this study demonstrate the unique potential of natural template-based peptide design for chemical biology and medicinal chemistry.

Computational Resources for MHC Ligand Identification

Advances in the high‐throughput determination of functional modulators of major histocompatibility complex (MHC) and improved computational predictions of MHC ligands have rendered the rational design of immunomodulatory peptides feasible. Proteome‐derived peptides and ‘reverse vaccinology’ by computational means will play a driving role in future vaccine design. Here we review the molecular mechanisms of the MHC mediated immune response, present the computational approaches that have emerged in this area of biotechnology, and provide an overview of publicly available computational resources for predicting and designing new peptidic MHC ligands.

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.