RESUMEN
The purpose of this study was to investigate the effects of caffeine consume on substrate metabolism and acute hormonal responses to a single bout of resistance exercise (RE). Ten resistance-trained men participated in this study. All subjects performed one repetition maximum (1RM) test and then performed two protocols: caffeine (CAF, 6 mg·kg(-1)) and control (CON) in counter balanced order. Subjects performed RE (8 exercises, 3 sets of 10 repetitions at 75% of 1RM) after caffeine or placebo ingestion one hour prior to RE. Blood samples collected prior to treatment ingestion (pre-60), immediately prior to RE (pre-exe), and 0, 15, 30 min post to RE (P0, P15, P30) for analysis of insulin, testosterone, cortisol, growth hormone, glucose, free fatty acid and lactic acid. Each experiment was separated by seven days. In this study, statistical analysis of a two-way analysis of variance (treatment by time) with repeated measures was applied. After ingesting caffeine, the concentrations of free fatty acid (pre- exe, P0, P15, P30) in CAF were significantly higher than CON (p < 0.05). Additionally, the responses of GH (P0, P15, P30) in CAF were significantly lower than CON (p < 0.05), whereas the concentrations of insulin, testosterone and cortisol were not different between CAF and CON (p < 0.05) after RE. The results of this study indicated that caffeine ingestion prior to RE might attenuate the response of GH. This effect might be caused by the elevation in blood FFA concentration at the beginning of RE. Key pointsCaffeine ingestion may attenuate the response of GH to a single bout of resistance exercise.The depression of GH response may be caused by the elevation in serum FFA concentration at the beginning of resistance exercise.Caffeine ingestion before resistance exercise may not alert the concentration of cortisol and testosterone.
RESUMEN
Discovering amino acid (AA) patterns on protein binding sites has recently become popular. We propose a method to discover the association relationship among AAs on binding sites. Such knowledge of binding sites is very helpful in predicting protein-protein interactions. In this paper, we focus on protein complexes which have protein-protein recognition. The association rule mining technique is used to discover geographically adjacent amino acids on a binding site of a protein complex. When mining, instead of treating all AAs of binding sites as a transaction, we geographically partition AAs of binding sites in a protein complex. AAs in a partition are treated as a transaction. For the partition process, AAs on a binding site are projected from three-dimensional to two-dimensional. And then, assisted with a circular grid, AAs on the binding site are placed into grid cells. A circular grid has ten rings: a central ring, the second ring with 6 sectors, the third ring with 12 sectors, and later rings are added to four sectors in order. As for the radius of each ring, we examined the complexes and found that 10Å is a suitable range, which can be set by the user. After placing these recognition complexes on the circular grid, we obtain mining records (i.e. transactions) from each sector. A sector is regarded as a record. Finally, we use the association rule to mine these records for frequent AA patterns. If the support of an AA pattern is larger than the predetermined minimum support (i.e. threshold), it is called a frequent pattern. With these discovered patterns, we offer the biologists a novel point of view, which will improve the prediction accuracy of protein-protein recognition. In our experiments, we produced the AA patterns by data mining. As a result, we found that arginine (arg) most frequently appears on the binding sites of two proteins in the recognition protein complexes, while cysteine (cys) appears the fewest. In addition, if we discriminate the shape of binding sites between concave and convex further, we discover that patterns {arg, glu, asp} and {arg, ser, asp} on the concave shape of binding sites in a protein more frequently (i.e. higher probability) make contact with {lys} or {arg} on the convex shape of binding sites in another protein. Thus, we can confidently achieve a rate of at least 78%. On the other hand {val, gly, lys} on the convex surface of binding sites in proteins is more frequently in contact with {asp} on the concave site of another protein, and the confidence achieved is over 81%. Applying data mining in biology can reveal more facts that may otherwise be ignored or not easily discovered by the naked eye. Furthermore, we can discover more relationships among AAs on binding sites by appropriately rotating these residues on binding sites from a three-dimension to two-dimension perspective. We designed a circular grid to deposit the data, which total to 463 records consisting of AAs. Then we used the association rules to mine these records for discovering relationships. The proposed method in this paper provides an insight into the characteristics of binding sites for recognition complexes.