Combining molecular dynamics and docking simulations to develop targeted protocols for performing optimized virtual screening campaigns on the HTRPM8 channel

Background: There is an increasing interest in TRPM8 ligands of medicinal interest, the rational design of which can be nowadays supported by structure-based in silico studies based on the recently resolved TRPM8 structures. Methods: The study involves the generation of a reliable hTRPM8 homology model, the reliability of which was assessed by a 1.0 \u3bcs MD simulation which was also used to generate multiple receptor conformations for the following structure-based virtual screening (VS) campaigns; docking simulations utilized different programs and involved all monomers of the selected frames; the so computed docking scores were combined by consensus approaches based on the EFO algorithm. Results: The obtained models revealed very satisfactory performances; LiGen\u2122 provided the best results among the tested docking programs; the combination of docking results from the four monomers elicited a markedly beneficial effect on the computed consensus models. Conclusions: The generated hTRPM8 model appears to be amenable for successful structure-based VS studies; cross-talk modulating effects between interacting monomers on the binding sites can be accounted for by combining docking simulations as performed on all the monomers; this strategy can have general applicability for docking simulations involving quaternary protein structures with multiple identical binding pockets

Data from docking simulations to develop an efficient strategy able to evaluate the interactions between RAGE and MDA-induced albumin adducts

This data article contains the results of docking simulations performed in order to develop a suitable in silico strategy able to assess the stability of the putative complexes between RAGE and MDA induced adducts on human albumin as experimentally determined doi: 10.1016/j.redox.2016.12.017, (Degani et al., 2017) [1]. The docking simulations involved different approaches to give a simplified yet realistic representation of the protein adducts and their environment. With increasing complexity, simulations involved the corresponding albumin tripeptides and pentapeptides with the modified residue in the central position as well as pseudo-structures which were generated by collecting the albumin residues around the adducted residue within a sphere of 7.5 \uc5 and 5 \uc5 radius. The reliability of the tested approaches was assessed by monitoring the score differences between adducted and unmodified residues. The obtained results revealed the greater predictive power of the spherical pseudo-structures compared to the simple tri- or pentapeptidic sequences thus suggesting that RAGE recognition involves residues which are spatially close to the modified residue even though not necessarily adjacent in the primary sequence

Development of a direct ESI-MS method for measuring the tannin precipitation effect of proline-rich peptides and in silico studies on the proline role in tannin-protein interactions

Tannins are a heterogeneous class of polyphenols that are present in several plants and foods. Their ability to interact and precipitate proline-rich proteins leads to different effects such as astringency or antidiarrheal activity. Thus, evaluation of the tannin content in plant extracts plays a key role in understanding their potential use as pharmaceuticals and nutraceuticals. Several methods have been proposed to study tannin-protein interactions but few of them are focused on quantification. The purpose of the present work is to set up a suitable and time efficient method able to quantify the extent of tannin protein precipitation. Bradykinin, chosen as a model, was incubated with increasing concentrations of 1,2,3,4,6-penta-O-galloyl-\u3b2-D-glucose and tannic acid selected as reference of tannic compounds. Bradykinin not precipitated was determined by a mass spectrometer TSQ Quantum Ultra Triple Quadrupole (direct infusion analysis). The results were expressed as PC 50 , which is the concentration able to precipitate 50% of the protein. The type of tannin-protein interaction was evaluated also after precipitate solubilisation. The involvement of proline residues in tannin-protein interactions was confirmed by repeating the experiment using a synthesized peptide (RR-9) characterized by the same bradykinin sequence, but having proline residues replaced by glycine residues: no interaction occurred between the peptide and the tannins. Moreover, modelling studies on PGG-BK and PGG-RR-9 were performed to deeply investigate the involvement of prolines: a balance of hydrophobic and H-bond contacts stabilizes the PGG-BK cluster and the proline residues exert a crucial role thus allowing the PGG molecules to elicit a sticking effect

Two peptides from soy β-conglycinin induce a hypocholesterolemic effect in hepG2 cells by a statin-like mechanism: comparative in vitro and in silico modeling studies

Two peptides from soybean \u3b2-conglycinin, i.e., YVVNPDNDEN (peptide 2) and YVVNPDNNEN (peptide 3), are known to be absorbed by human enterocytes. The former is a fragment of LRVPAGTTFYVVNPDNDENLRMIA (peptide 1), previously shown to increase the low-density lipoprotein (LDL) uptake and degradation in hepatocytes. Research carried out in silico on their interactions with the catalytic site of 3-hydroxy-3-methylglutaryl CoA reductase (HMGCoAR) demonstrated that they behave as competitive inhibitors of HMGCoAR activity with a statin-like mechanism, confirmed by direct inhibition experiments. Research in HepG2 cells aimed at investigating the effects of these peptides on cholesterol metabolism showed that compared to mock treatment peptide 2 at 350 \u3bcM up-regulates the mature SREBP2 protein level by 134.0 \ub1 10.5%, increases the LDLR protein level by 152.0 \ub1 20.0%, and enhances the HMGCoAR protein production by 171 \ub1 29.9%, whereas peptide 3 up-regulates the mature SREBP2 protein level by 158.0 \ub1 9.2%, increases the LDL level 164.0 \ub1 17.9%, and induces a HMGCoAR protein increase by 170 \ub1 50.0%

Additional modifications to the Blumgart pancreaticojejunostomy: Results of a propensity score-matched analysis versus Cattel-Warren pancreaticojejunostomy

Background: Postoperative pancreatic fistula continues to occur frequently after pancreatoduodenectomy. Methods: We have described a modification of the Blumgart pancreaticojejunostomy. The modification of the Blumgart pancreaticojejunostomy was compared to the Cattel-Warren pancreaticojejunostomy in cohorts of patients matched by propensity scores based on factors predictive of clinically relevant postoperative pancreatic fistula, which was the primary endpoint of this study. Based on a noninferiority study design, 95 open pancreatoduodenectomies per group were needed. Feasibility of the modification of the Blumgart pancreaticojejunostomy in robotic pancreatoduodenectomy was also shown. All pancreaticojejunostomies were performed by a single surgeon. Results: Between October 2011 and May 2019, there were 415 pancreatoduodenectomies with either a Cattel-Warren pancreaticojejunostomy (n = 225) or a modification of the Blumgart pancreaticojejunostomy (n = 190). There was 1 grade C postoperative pancreatic fistula in 190 consecutive modification of the Blumgart pancreaticojejunostomies (0.5%). Logistic regression analysis showed that the rate of clinically relevant postoperative pancreatic fistula was not affected by consecutive case number. After exclusion of robotic pancreatoduodenectomies (the Cattel-Warren pancreaticojejunostomy: 82; modification of the Blumgart pancreaticojejunostomy: 66), 267 open pancreatoduodenectomies were left, among which the matching process identified 109 pairs. The modification of the Blumgart pancreaticojejunostomy was shown to be noninferior to the Cattel-Warren pancreaticojejunostomy with respect to clinically relevant postoperative pancreatic fistula (11.9% vs 22.9%; odds ratio: 0.46 [0.21–0.93]; P = .03), grade B postoperative pancreatic fistula (11.9% vs 18.3%; P = .18), and grade C postoperative pancreatic fistula (0 vs 4.6%; P = .05) as well as to all secondary study endpoints. The modification of the Blumgart pancreaticojejunostomy was feasible in 66 robotic pancreatoduodenectomies. In this subgroup with 1 conversion to open surgery (1.5%), a clinically relevant postoperative pancreatic fistula occurred after 9 procedures (13.6%) with no case of grade C postoperative pancreatic fistula and a 90-day mortality of 3%. Conclusion: The modification of the Blumgart pancreaticojejunostomy described herein is noninferior to the Cattel-Warren pancreaticojejunostomy in open pancreatoduodenectomy. This technique is also feasible in robotic pancreatoduodenectomy

Phenolic extracts from extra virgin olive oils inhibit dipeptidyl peptidase iv activity: In vitro, cellular, and in silico molecular modeling investigations

Two extra virgin olive oil (EVOO) phenolic extracts (BUO and OMN) modulate DPP-IV activity. The in vitro DPP-IV activity assay was performed at the concentrations of 1, 10, 100, 500, and 1000 μg/mL, showing a dose-dependent inhibition by 6.8 ± 1.9, 17.4 ± 6.1, 37.9 ± 2.4, 57.8 ± 2.9, and 81 ± 1.4% for BUO and by 5.4 ± 1.7, 8.9 ± 0.4, 28.4 ± 7.2, 52 ± 1.3, and 77.5 ± 3.5% for OMN. Moreover, both BUO and OMN reduced the DPP-IV activity expressed by Caco-2 cells by 2.9 ± 0.7, 44.4 ± 0.7, 61.2 ± 1.8, and 85 ± 4.2% and by 3 ± 1.9, 35 ± 9.4, 60 ± 7.2, and 82 ± 2.8%, respectively, at the same doses. The concentration of the most abundant and representative secoiridoids within both extracts was analyzed by nuclear magnetic resonance ((1)H-NMR). Oleuropein, oleacein, oleocanthal, hydroxytyrosol, and tyrosol, tested alone, reduced the DPP-IV activity, with IC(50) of 472.3 ± 21.7, 187 ± 11.4, 354.5 ± 12.7, 741.6 ± 35.7, and 1112 ± 55.6 µM, respectively. Finally, in silico molecular docking simulations permitted the study of the binding mode of these compounds

Design, synthesis and preliminary biological evaluation of 3-cyclopropyl-4-phenoxy-1H-pyrazole derivatives as small molecular ligands of RAGE

Receptor for advanced glycation end products (RAGE) is a multiligand receptor belonging to the immunoglobulin superfamily and plays a crucial role in the development of many human diseases such as neurodegenerative diseases, diabetes, cardiovascular diseases and cancer.1 RAGE is involved in a number of cell processes such as neuroinflammation, apoptosis, proliferation and autophagy, and therefore it is of considerable interest as a promising drug target for innovative therapeutic approaches. It consists of an extracellular region, a short hydrophobic transmembrane spanning region, and a highly charged amino acid cytoplasmatic tail. The extracellular region contains a signal peptide, followed by one N-terminal V-type immunoglobulin domain and two C-type (C1 and C2) immunoglobulin domains.2 RAGE is able to interact with a large number of pro-inflammatory and regulatory molecules, such as advanced glycation end-products (AGEs), quinolinic acid, beta amyloid (A\u3b2), high mobility group box 1 (HMGB1), S100/calgranulin family proteins.3,4 However, due to the structural heterogeneity of these endogenous ligands, little is known about the key pharmacophore elements for ligand-RAGE interaction and the specific mode of binding. On these grounds, we aimed at designing new small molecules able to bind the VC1 extracellular domains of RAGE, in order to clarify the structural features that account for RAGE affinity and activation, and to identify new drug-like compounds. Following a process of structural simplification of known pyrazole-5-carboxamide RAGE ligands,1 we planned a set of novel derivatives characterized by a variously functionalized 3-cyclopropyl-4-phenoxy-1H-pyrazole scaffold (Figure 1). The design and synthesis of the new putative RAGE ligands will be presented and discussed, together with the results of their in vitro screening by means of a surface plasmon resonance (SPR)-based assay to estimate their binding ability to the RAGE extracellular domain. References 1. Bongarzone S., Savickas V., Luzi F., Gee A. D. J. Med. Chem. 2017, 60, 7213-7232. 2. Hudson B. I., Carter A. M., Harja E., Kalea A. Z., Arriero M., Yang H., Grant P. J., Schmidt A. M. FASEB J. 2008, 22, 1572-1580. 3. Xue J., Rai V., Singer D., Chabierski S., Xie J., Reverdatto S., Burz D. S., Schmidt A. M., Hoffmann R., Shekhtman A. Structure 2011, 19, 722\u2013732. 4. Koch M., Chitayat S., Dattilo B. M., Schiefner A., Diez J., Chazin W. J., Fritz, G. Structure 2010, 18, 1342-1352