It has been shown that the progress in the determination of membrane protein structure grows exponentially, with approximately the same growth rate as that of the water-soluble proteins. In order to investigate the ef...It has been shown that the progress in the determination of membrane protein structure grows exponentially, with approximately the same growth rate as that of the water-soluble proteins. In order to investigate the effect of this, on the performance of prediction algorithms for both α-helical and β-barrel membrane proteins, we conducted a prospective study based on historical records. We trained separate hidden Markov models with different sized training sets and evaluated their performance on topology prediction for the two classes of transmembrane proteins. We show that the existing top-scoring algorithms for predicting the transmembrane segments of α-helical membrane proteins perform slightly better than that of β-barrel outer membrane proteins in all measures of accuracy. With the same rationale, a metaoanalysis of the performance of the secondary structure prediction algorithms indicates that existing algorithmic techniques cannot be further improved by just adding more non-homologous sequences to the training sets. The upper limit for secondary structure prediction is estimated to be no more than 70% and 80% of correctly predicted residues for single sequence based methods and multiple sequence based ones, respectively. Therefore, we should concentrate our efforts on utilizing new techniques for the development of even better scoring predictors.展开更多
Estrogen mediates multiple functions in the brain through the interaction of estrogen receptor (ER)α and ERβ with a host of nuclear proteins that regulate specific gene transcription. We have identified ERAP 140, AI...Estrogen mediates multiple functions in the brain through the interaction of estrogen receptor (ER)α and ERβ with a host of nuclear proteins that regulate specific gene transcription. We have identified ERAP 140, AIB 1, Trk A, Src, pCREB and CREB as ERβ interacting proteins in the mouse brain. Earlier we showed that the interaction of ERβ with ERAP 140 decreased whereas its expression increased with aging in the brain of female mice. Here we report that the pattern of interaction and expression is different in male mice as compared to females. The interaction of ERAP 140 with ERβ decreased in adult male mouse brain as compared to young and remained almost similar in old whereas its expression was higher in adult than young and old, which were almost similar. Further in silico secondary structure analysis by self-optimized prediction method alignment (SOPMA) and PSIPRED revealed that ERβ interacting proteins were rich in alpha helices and coils. Such findings might help to design ER modulators which can regulate specific functions of estrogen in the brain during aging and degenerative diseases.展开更多
基金PGB was supported by a scholarship from the State Scholarships Foundation of Greece (SSF) for postdoctoral research in the Department of Cell Biology and Biophysics of the University of Athens (Machine Learning Algorithms for Bioinformatics)
文摘It has been shown that the progress in the determination of membrane protein structure grows exponentially, with approximately the same growth rate as that of the water-soluble proteins. In order to investigate the effect of this, on the performance of prediction algorithms for both α-helical and β-barrel membrane proteins, we conducted a prospective study based on historical records. We trained separate hidden Markov models with different sized training sets and evaluated their performance on topology prediction for the two classes of transmembrane proteins. We show that the existing top-scoring algorithms for predicting the transmembrane segments of α-helical membrane proteins perform slightly better than that of β-barrel outer membrane proteins in all measures of accuracy. With the same rationale, a metaoanalysis of the performance of the secondary structure prediction algorithms indicates that existing algorithmic techniques cannot be further improved by just adding more non-homologous sequences to the training sets. The upper limit for secondary structure prediction is estimated to be no more than 70% and 80% of correctly predicted residues for single sequence based methods and multiple sequence based ones, respectively. Therefore, we should concentrate our efforts on utilizing new techniques for the development of even better scoring predictors.
文摘Estrogen mediates multiple functions in the brain through the interaction of estrogen receptor (ER)α and ERβ with a host of nuclear proteins that regulate specific gene transcription. We have identified ERAP 140, AIB 1, Trk A, Src, pCREB and CREB as ERβ interacting proteins in the mouse brain. Earlier we showed that the interaction of ERβ with ERAP 140 decreased whereas its expression increased with aging in the brain of female mice. Here we report that the pattern of interaction and expression is different in male mice as compared to females. The interaction of ERAP 140 with ERβ decreased in adult male mouse brain as compared to young and remained almost similar in old whereas its expression was higher in adult than young and old, which were almost similar. Further in silico secondary structure analysis by self-optimized prediction method alignment (SOPMA) and PSIPRED revealed that ERβ interacting proteins were rich in alpha helices and coils. Such findings might help to design ER modulators which can regulate specific functions of estrogen in the brain during aging and degenerative diseases.
