No abstract

Human neutrophil elastase (HNE), a main actor in the development of chronic obstructive pulmonary diseases, has been recently involved in non-small cell lung cancer progression. It can act at several levels (i) intracellularly, cleaving for instance the adaptor molecule insulin receptor substrate-1 (IRS-1) (ii) at the cell surface, hydrolyzing receptors as CD40 (iii) in the extracellular space, generating elastin fragments i.e. morphoelastokines which potently stimulate cancer cell invasiveness and angiogenesis. Since decades, researchers identified natural compounds and/or synthesized agents which antagonize HNE activity that will be described in this review article. Some of these compounds might be of value as therapeutic agents in lung cancer. However, it is now widely accepted that lung tumor invasion and metastasis involve proteolytic cascades. Accordingly, we will here mainly focus our attention to natural substances able to display a dual inhibitory capacity (i.e. lipids and derivatives, phenolics) towards HNE and matrix metalloproteinases (MMPs), particularly MMP-2. To that purpose, we recently synthesize substances named "LipoGalardin" (Moroy G. et al., Biochem. Pharmacol., 2011, 81(5), 626-635) exhibiting such inhibitory bifunctionality. At last, we will propose an original synthetic scheme for designing a potent biheaded HNE/MMP-2 inhibitor.

Peptide–protein interactions are important to many processes of life, particularly for signal transmission or regulatory mechanisms. When no information is known about the interaction between a protein and a peptide, it is of interest to propose candidate sites of interaction at the protein surface, to assist the design of biological experiments to probe the interaction, or to serve as a starting point for more focused in silico approaches. PEP-SiteFinder is a tool that will, given the structure of a protein and the sequence of a peptide, identify protein residues predicted to be at peptide–protein interface. PEP-SiteFinder relies on the 3D de novo generation of peptide conformations given its sequence. These conformations then undergo a fast blind rigid docking on the complete protein surface, and we have found, as the result of a benchmark over 41 complexes, that the best poses overlap to some extent the experimental patch of interaction for close to 90% complexes. In addition, PEP-SiteFinder also returns a propensity index we have found informative about the confidence of the prediction. The PEP-SiteFinder web server is available at http://bioserv.rpbs.univ-paris-diderot.fr/PEP-SiteFinder.

The proteins of the Bcl-2 family are important regulators of apoptosis, or programmed cell death. These proteins regulate this fundamental biological process via the formation of heterodimers involving both pro-and anti-apoptotic family members. Disruption of the balance between anti-and pro-apoptotic Bcl-2 proteins is the cause of numerous pathologies. Bcl-xl, an anti-apoptotic protein of this family, is known to form heterodimers with multiple pro-apoptotic proteins, such as Bad, Bim, Bak, and Bid. To elucidate the molecular basis of this recognition process, we used molecular dynamics simulations coupled with the Molecular Mechanics/Poisson-Boltzmann Surface Area approach to identify the amino acids that make significant energetic contributions to the binding free energy of four complexes formed between Bcl-xl and pro-apoptotic Bcl-2 homology 3 peptides. A fifth protein-peptide complex composed of another anti-apoptotic protein, Bcl-w, in complex with the peptide from Bim was also studied. The results identified amino acids of both the anti-apoptotic proteins as well as the Bcl-2 homology 3 (BH3) domains of the pro-apoptotic proteins that make strong, recurrent interactions in the protein complexes. The calculations show that the two anti-apoptotic proteins, Bcl-xl and Bcl-w, share a similar recognition mechanism. Our results provide insight into the molecular basis for the promiscuous nature of this molecular recognition process by members of the Bcl-2 protein family. These amino acids could be targeted in the design of new mimetics that serve as scaffolds for new antitumoral molecules.

The degradation of elastin, the insoluble biopolymer of tropoelastin, can lead to the production of small peptides. These elastin-derived peptides (EDPs) are playing a key role in cellular behavior within the extracellular matrix, showing a great variety of biological effects such as chemotaxis, stimulation of cell proliferation, ion flux modifications, vasorelaxation, and inflammatory enzymes secretion. It has also been demonstrated recently that EDPs containing the GXXPG motif could induce pro-MMP1 and pro-MMP3 upregulation. Elastolysis could then cause collagen degradation and play an important role in the aging process. Many experimental studies have been devoted to EDPs, but their structure/activity relationships are not well elucidated yet. However, the assumption that their active conformation is a type VIII beta-turn on GXXP was highly suggested on the basis of predictive statistical calculations. Investigation of the EDPs three-dimensional (3D) structure would provide useful information for drug-design strategies to propose specific inhibitors. The work presented here reports theoretical results obtained from molecular dynamics simulations performed over 128 human EDPs containing the GXXP motif. We show that all the peptides, for which the central residues are not glycines, adopt a canonical (or very close to) type VIII beta-turn structure on the GXXP sequence. Amino acids surrounding this motif are also important for the structural behavior. Any residue located before the GXXP motif (XGXXP) increases the beta-turn stabilization, whereas the residue located after GXXP (GXXPX) has no significant structural effect. Moreover, we show their biological activity can be correlated with their ability to exhibit a type VIII beta-turn conformation.

Mutations in the gene for fibrillin-1 cause Marfan syndrome (MFS), a common hereditary disorder of connective tissue. Recent findings suggest that proteolysis, increased matrix metalloproteinase activity, and fragmentation of fibrillin-rich microfibrils in tissues of persons with MFS contribute to the complex pathogenesis of this disorder. In this study we show that a fibrillin-1 fragment containing a EGFEPG sequence that conforms to a putative GxxPG elastin-binding protein (EBP) consensus sequence upregulates the expression and production of matrix metalloproteinase (MMP)-1 by up to ninefold in a cell culture system. A mutation of the GxxPG consensus sequence site abrogated the effects. This is the first demonstration of such an effect for ligands other than elastin fragments. Molecular dynamics analysis of oligopeptides with the wildtype and mutant sequence support our biochemical results by predicting significant alterations of structural characteristics such as the potential for forming a type VIII beta-turn that are thought to be important for binding to the EBP. These results suggest that fibrillin-1 fragments may regulate MMP-1 expression, and that the dysregulation of MMPs related to fragmentation of fibrillin might contribute to the development of MFS. Our Gene Ontology (GO) analysis of the human proteome shows that proteins with multiple GxxPG motifs are highly enriched for GO terms related to the extracellular matrix. Matrix proteins with multiple GxxPG sites include fibrillin-1, -2, and -3, elastin, fibronectin, laminin, and several tenascins and collagens. Some of these proteins have been associated with disorders involving alterations in MMP regulation, and the results of the present study suggest a potential mechanism for these observations.

Epigallocatechin gallate (EGCG) is an inhibitor of DYRK1A, a serine/threonine kinase considered to be a major contributor of cognitive dysfunctions in Down syndrome (DS). Two clinical trials in adult patients with DS have shown the safety and efficacy to improve cognitive phenotypes using commercial green tea extract containing EGCG (45% content). In the present study, we performed a preclinical study using FontUp®, a new nutritional supplement with a chocolate taste specifically formulated for the nutritional needs of patients with DS and enriched with a standardized amount of EGCG in young mice overexpressing Dyrk1A (TgBACDyrk1A). This preparation is differential with previous one used, because its green tea extract has been purified to up 94% EGCG of total catechins. We analyzed the in vitro effect of green tea catechins not only for EGCG, but for others residually contained in FontUp®, on DYRK1A kinase activity. Like EGCG, epicatechin gallate was a noncompetitive inhibitor against ATP, molecular docking computations confirming these results. Oral FontUp® normalized brain and plasma biomarkers deregulated in TgBACDyrk1A, without negative effect on liver and cardiac functions. We compared the bioavailability of EGCG in plasma and brain of mice and have demonstrated that EGCG had well crossed the blood-brain barrier.

We developed an original in silico approach for the prediction of CYP2D6 inhibition combining the knowledge of the protein structure and its dynamic behavior in response to the binding of various ligands and machine learning modeling. This approach includes structural information for CYP2D6 based on the available crystal structures and molecular dynamic simulations (MD) that we performed to take into account conformational changes of the binding site. We performed modeling using three learning algorithms--support vector machine, RandomForest and NaiveBayesian--and we constructed combined models based on topological information of known CYP2D6 inhibitors and predicted binding energies computed by docking on both X-ray and MD protein conformations. In addition, we identified three MD-derived structures that are capable all together to better discriminate inhibitors and non-inhibitors compared with individual CYP2D6 conformations, thus ensuring complementary ligand profiles. Inhibition models based on classical molecular descriptors and predicted binding energies were able to predict CYP2D6 inhibition with an accuracy of 78% on the training set and 75% on the external validation set.