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Phelps CB Sengchanthalangsy LL Huxford T Ghosh G 《The Journal of biological chemistry》2000,275(38):29840-29846
X-ray crystal structures of the NF-kappa B.I kappa B alpha complex revealed an extensive and complex protein-protein interface involving independent structural elements present in both I kappa B alpha and NF-kappa B. In this study, we employ a gel electrophoretic mobility shift assay to assess and quantitate the relative contributions of the observed interactions toward overall complex binding affinity. I kappa B alpha preferentially binds to the p50/p65 heterodimer and p65 homodimer, with binding to p50 homodimer being significantly weaker. Our results indicate that the nuclear localization sequence and the region C-terminal to it of the NF-kappa B p65 subunit is a major contributor to NF-kappa B. I kappa B alpha complex formation. Additionally, there are no contacts between the corresponding nuclear localization signal tetrapeptide of p50 and I kappa B alpha. A second set of interactions involving the acidic C-terminal/PEST-like region of I kappa B alpha and the NF-kappa B p65 subunit N-terminal domain also contributes binding energy toward formation of the complex. This interaction is highly dynamic and nonspecific in nature, as shown by oxidative cysteine cross-linking. Phosphorylation of the C-terminal/PEST-like region by casein kinase II further enhances binding. 相似文献
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Distance-based methods are popular for reconstructing evolutionary trees thanks to their speed and generality. A number of methods exist for estimating distances from sequence alignments, which often involves some sort of correction for multiple substitutions. The problem is to accurately estimate the number of true substitutions given an observed alignment. So far, the most accurate protein distance estimators have looked for the optimal matrix in a series of transition probability matrices, e.g. the Dayhoff series. The evolutionary distance between two aligned sequences is here estimated as the evolutionary distance of the optimal matrix. The optimal matrix can be found either by an iterative search for the Maximum Likelihood matrix, or by integration to find the Expected Distance. As a consequence, these methods are more complex to implement and computationally heavier than correction-based methods. Another problem is that the result may vary substantially depending on the evolutionary model used for the matrices. An ideal distance estimator should produce consistent and accurate distances independent of the evolutionary model used. 相似文献7.
Background
Profile hidden Markov model (HMM) techniques are among the most powerful methods for protein homology detection. Yet, the critical features for successful modelling are not fully known. In the present work we approached this by using two of the most popular HMM packages: SAM and HMMER. The programs' abilities to build models and score sequences were compared on a SCOP/Pfam based test set. The comparison was done separately for local and global HMM scoring. 相似文献8.
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