Martin Grigorov

Flavonoids for Controlling Starch Digestion: Structural Requirements for Inhibiting Human α-Amylase

In this study we investigated the structural requirements for inhibition of human salivary alpha-amylase by flavonoids. Four flavonols and three flavones, out of the 19 flavonoids tested, exhibited IC50 values less than 100 microM against human salivary alpha-amylase activity. Structure-activity relationships of these inhibitors by computational ligand docking showed that the inhibitory activity of flavonols and flavones depends on (i) hydrogen bonds between the hydroxyl groups of the polyphenol ligands and the catalytic residues of the binding site and (ii) formation of a conjugated pi-system that stabilizes the interaction with the active site. Our findings show that certain naturally occurring flavonoids act as inhibitors of human alpha-amylase, which makes them promising candidates for controlling the digestion of starch and postprandial glycemia.

INTERACTION OF POSITIONAL ISOMERS OF QUERCETIN GLUCURONIDES WITH THE TRANSPORTER ABCC2 (CMOAT, MRP2)

The exporter ABCC2 (cMOAT, MRP2) is a membrane-bound protein on the apical side of enterocytes and hepatic biliary vessels that transports leukotriene C4, glutathione, some conjugated bile salts, drugs, xenobiotics, and phytonutrients. The latter class includes quercetin, a bioactive flavonoid found in foods such as onions, apples, tea, and wine. There is no available three-dimensional (3D) structure of ABCC2. We have predicted the 3D structure by in silico modeling, showing that 3-[[3-[2-(7-chloroquinolin-2-yl)vinyl]phenyl]-(2-dimethylcarbamoylethylsulfanyl)methylsulfanyl] propionic acid (MK571) binds most tightly to the putative binding site, and then tested the computational prediction experimentally by measuring interaction with all quercetin monoglucuronides occurring in vivo (quercetin substituted with glucuronic acid at the 3-, 3′-, 4′-, and 7-hydroxyl groups). The 4′-O-β-D-glucuronide is predicted in silico to interact most strongly and the 3-O-β-D-glucuronide most weakly, and this prediction is supported experimentally using binding and competition assays on ABCC2-overexpressing baculovirus-infected Sf9 cells. To test the transport in situ, we examined the effect of two ABCC2 inhibitors, MK571 and cyclosporin A, on the transport into the media of quercetin glucuronides produced intracellularly by Caco2 cells. The inhibitors reduced the amount of all quercetin glucuronides in the media. The results show that the molecular model of ABCC2 agrees well with experimentally determined ABCC2-ligand interactions and, importantly, that the interaction of ABCC2 with quercetin glucuronides is dependent on the position and nature of substitution.

Similarity networks of protein binding sites

An increasing attention has been dedicated to the characterization of complex networks within the protein world. This work is reporting how we uncovered networked structures that reflected the structural similarities among protein binding sites. First, a 211 binding sites dataset has been compiled by removing the redundant proteins in the Protein Ligand Database (PLD) (http://www‐mitchell.ch.cam.ac.uk/pld/). Using a clique detection algorithm we have performed all‐against‐all binding site comparisons among the 211 available ones. Within the set of nodes representing each binding site an edge was added whenever a pair of binding sites had a similarity higher than a threshold value. The generated similarity networks revealed that many nodes had few links and only few were highly connected, but due to the limited data available it was not possible to definitively prove a scale‐free architecture. Within the same dataset, the binding site similarity networks were compared with the networks of sequence and fold similarity networks. In the protein world, indications were found that structure is better conserved than sequence, but on its own, sequence was better conserved than the subset of functional residues forming the binding site. Because a binding site is strongly linked with protein function, the identification of protein binding site similarity networks could accelerate the functional annotation of newly identified genes. In view of this we have discussed several potential applications of binding site similarity networks, such as the construction of novel binding site classification databases, as well as the implications for protein molecular design in general and computational chemogenomics in particular. Proteins 2006.

Global properties of biological networks.

This article discusses the most recent achievements in understanding the biological implications of the small-world and scale-free global topological properties of genetic, proteomic and metabolic networks. Most importantly, these networks are highly clustered and have small node-to-node distances. With their few very connected nodes, which are statistically unlikely to fail under random conditions, the proper functioning of these systems is maintained under external perturbations.

Descriptor-based protein remote homology identification

Here, we report a novel protein sequence descriptor‐based remote homology identification method, able to infer fold relationships without the explicit knowledge of structure. In a first phase, we have individually benchmarked 13 different descriptor types in fold identification experiments in a highly diverse set of protein sequences. The relevant descriptors were related to the fold class membership by using simple similarity measures in the descriptor spaces, such as the cosine angle. Our results revealed that the three best‐performing sets of descriptors were the sequence‐alignment‐based descriptor using PSI‐BLAST e‐values, the descriptors based on the alignment of secondary structural elements (SSEA), and the descriptors based on the occurrence of PROSITE functional motifs. In a second phase, the three top‐performing descriptors were combined to obtain a final method with improved performance, which we named DescFold. Class membership was predicted by Support Vector Machine (SVM) learning. In comparison with the individual PSI‐BLAST‐based descriptor, the rate of remote homology identification increased from 33.7% to 46.3%. We found out that the composite set of descriptors was able to identify the true remote homolog for nearly every sixth sequence at the 95% confidence level, or some 10% more than a single PSI‐BLAST search. We have benchmarked the DescFold method against several other state‐of‐the‐art fold recognition algorithms for the 172 LiveBench‐8 targets, and we concluded that it was able to add value to the existing techniques by providing a confident hit for at least 10% of the sequences not identifiable by the previously known methods.

Computational Studies of Ligand-Receptor Interactions in Bitter Taste Receptors

Phenylthiocarbamide tastes intensely bitter to some individuals, but others find it completely tasteless. Recently, it was suggested that phenylthiocarbamide elicits bitter taste by interacting with a human G protein-coupled receptor (hTAS2R38) encoded by the PTC gene. The phenylthiocarbamide nontaster trait was linked to three single nucleotide polymorphisms occurring in the PTC gene. Using the crystal structure of bovine rhodopsin as template, we generated the 3D structure of hTAS2R38 bitter taste receptor. We were able to map on the receptor structure the amino acids affected by the genetic polymorphisms and to propose molecular functions for two of them that explained the emergence of the nontaster trait. We used molecular docking simulations to find that phenylthiocarbamide exhibited a higher affinity for the target receptor than the structurally similar molecule 6-n-propylthiouracil, in line with recent experimental studies. A 3D model was constructed for the hTAS2R16 bitter taste receptor as well, by applying the same protocol. We found that the recently published experimental ligand binding affinity data for this receptor correlated well with the binding scores obtained from our molecular docking calculations.

An integrative metabolism approach identifies stearoyl-CoA desaturase as a target for an arachidonate-enriched diet

Epidemiological studies have correlated diets containing higher intakes of PUFA with lower rates of chronic metabolic diseases. The molecular mechanisms regulated by the consumption of PUFA were examined by using an integrative metabolism approach assaying the liver transcriptome and lipid‐metabolome of mice fed a control diet, an arachidonate (AA)‐enriched fungal oil, an eicosapentaenoic (EPA)/docosahexaenoic (DHA)‐enriched fish oil, or a combination of the two oils. Hepatic gene transcription and fatty acid (FA) metabolism were significantly altered by diets enriched with AA, as revealed by global error assessment and singular value decomposition (SVD) analysis, respectively. SVD analysis of the lipid data, reinforced with transcriptomics, suggests that the chronic feeding of AA modulates molecular endpoints similar to those previously reported in the obesity‐resistant SCD1−/− mouse, namely, genes involved in lipid oxidation/synthesis and the significant changes in FA metabolism stemming from a repressed SCD1 activity. Specifically, the total levels and FA composition of several phospholipid (PL) species were significantly changed, with phosphatidylcholine (PC) demonstrating the greatest alterations. Reduced PC levels were linked to decreased expression of enzymes in PC biosynthesis (choline kinase, −2.2‐fold; glycerol‐3‐phosphate acyltransferase, −2.0‐fold). Alterations in PL‐FA composition were related to decreased expression of FA biosynthetic genes [fatty acid synthetase, −3.7‐fold; stearoyl‐CoA desaturase‐1 (SCD1), −1.8‐fold]. Lower hepatic SCD1 gene expression levels were reflected in various aspects of FA metabolism through increased concentrations of palmitic (fungal oil, +45%; combination, +106%) and stearic acids (fungal oil, +60%; combination, +63%) in PC. Importantly, an integrated approach showed that these effects were not attenuated by the addition of an EPA/DHA‐enriched fish oil, thereby identifying a previously unrecognized and distinct role for AA in the regulation of hepatic lipid metabolism.

Integration of Structure−Activity Relationship and Artificial Intelligence Systems To Improve in Silico Prediction of Ames Test Mutagenicity

The Ames mutagenicity test in Salmonella typhimurium is a bacterial short-term in vitro assay aimed at detecting the mutagenicity caused by chemicals. Mutagenicity is considered as an early alert for carcinogenicity. After a number of decades, several (Q)SAR studies on this endpoint yielded enough evidence to make feasible the construction of reliable computational models for prediction of mutagenicity from the molecular structure of chemicals. In this study, we propose a combination of a fragment-based SAR model and an inductive database. The hybrid system was developed using a collection of 4337 chemicals (2401 mutagens and 1936 nonmutagens) and tested using 753 independent compounds (437 mutagens and 316 nonmutagens). The overall error of this system on the external test set compounds is 15% (sensitivity = 15%, specificity = 15%), which is quantitatively similar to the experimental error of Ames test data (average interlaboratory reproducibility determined by the National Toxicology Program). Moreover, each single prediction is provided with a specific confidence level. The results obtained give confidence that this system can be applied to support early and rapid evaluation of the level of mutagenicity concern.

Biomarkers of human gastrointestinal tract regions.

Dysregulation of intestinal epithelial cell performance is associated with an array of pathologies whose onset mechanisms are incompletely understood. While whole-genomics approaches have been valuable for studying the molecular basis of several intestinal diseases, a thorough analysis of gene expression along the healthy gastrointestinal tract is still lacking. The aim of this study was to map gene expression in gastrointestinal regions of healthy human adults and to implement a procedure for microarray data analysis that would allow its use as a reference when screening for pathological deviations. We analyzed the gene expression signature of antrum, duodenum, jejunum, ileum, and transverse colon biopsies using a biostatistical method based on a multivariate and univariate approach to identify region-selective genes. One hundred sixty-six genes were found responsible for distinguishing the five regions considered. Nineteen had never been described in the GI tract, including a semaphorin probably implicated in pathogen invasion and six novel genes. Moreover, by crossing these genes with those retrieved from an existing data set of gene expression in the intestine of ulcerative colitis and Crohn’s disease patients, we identified genes that might be biomarkers of Crohn’s and/or ulcerative colitis in ileum and/or colon. These include CLCA4 and SLC26A2, both implicated in ion transport. This study furnishes the first map of gene expression along the healthy human gastrointestinal tract. Furthermore, the approach implemented here, and validated by retrieving known gene profiles, allowed the identification of promising new leads in both healthy and disease states.

Putting the 'Ome' in lipid metabolism.

The recognition that altered lipid metabolism underlies many metabolic disorders challenging Western society highlights the importance of this metabolomic subset, herein referred to as the lipidome. Although comprehensive lipid analyses are not a recent concept, the novelty of a lipidomic approach lies with the application of robust statistical algorithms to highlight subtle, yet significant, changes in a population of lipid molecules. First-generation lipidomic studies have demonstrated the sensitivity of interpreting quantitative datasets with computational software; however, the innate power of comprehensive lipid profiling is often not exploited, as robust statistical models are not routinely utilized. Therefore, the current review aims to briefly describe the current technologies suitable for comprehensive lipid analysis, outline innovative mathematical models that have the ability to reveal subtle changes in metabolism, which will ameliorate our understanding of lipid biochemistry, and demonstrate the biological revelations found through lipidomic approaches and their potential implications for health management.