No abstract

Apoptosis proteins have a central role in the development and homeostasis of an organism. These proteins are very important for understanding the mechanism of programmed cell death. Many efforts in pharmaceutical research have been aimed at understanding their structure and function. Unfortunately, thus far, very few apoptosis protein structures have been determined. In contrast, many apoptosis protein sequences are known, and many more are expected to come in the near future. Because of the extremely unbalanced state, it would be worthwhile to develop a fast sequence-based method to identify their subcellular location so as to gain some insight about their biological function. In view of this, a study was initiated in an attempt to identify the subcellular location of apoptosis proteins according to their sequences by means of the covariant discriminant function, which was established based on the Mahalanobis distance and Chou's invariance theorem (Chou, Proteins 1995;21:319-344). The results were quite promising, indicating that the subcellular location of apoptosis proteins are predictable to a considerably accurate extent if a good training data set can be established. It is expected that, with a continuous improvement of the training data set by incorporating more and more new data, the current method might eventually become a useful tool in this area because the function of an apoptosis protein is closely related to its subcellular location.

The major heat shock protein, Hsp70, can protect against cell death by directly interfering with mitochondrial apoptosis pathways. However, Hsp70 also sensitizes cells to certain apoptotic stimuli like TNF. Little is known about how Hsp70 enhances apoptosis. We demonstrate here that Hsp70 promotes TNF killing by specifically binding the coiled-coil domain of IB kinase ␥ (IKK␥) to inhibit IKK activity and consequently inhibit NF-B-dependent antiapoptotic gene induction. An IKK␥ mutant, which interacts with Hsp70, competitively inhibits the Hsp70-IKK␥ interaction and relieves heat-mediated NF-B suppression. Depletion of Hsp70 expression with RNA interference rescues TNF-mediated cell death. Although TNF may or may not be sufficient to trigger apoptosis on its own, TNF-triggered apoptosis was initiated or made worse when Hsp70 expression increased to high levels to disrupt NF-B signaling. These results provide significant novel insights into the molecular mechanism for the pro-apoptotic behavior of Hsp70 in death-receptor-mediated cell death.

It has been quite clear that the success rate for predicting protein structural class can be improved significantly by using the algorithms that incorporate the coupling effect among different amino acid components of a protein. However, there is still a lot of confusion in understanding the relationship of these advanced algorithms, such as the least Mahalanobis distance algorithm, the component-coupled algorithm, and the Bayes decision rule. In this communication, a simple, rigorous derivation is provided to prove that the Bayes decision rule introduced recently for protein structural class prediction is completely the same as the earlier component-coupled algorithm. Meanwhile, it is also very clear from the derivative equations that the least Mahalanobis distance algorithm is an approximation of the component-coupled algorithm, also named as the covariant-discriminant algorithm introduced by Chou and Elrod in protein subcellular location prediction (Protein Engineering, 1999; 12:107-118). Clarification of the confusion will help use these powerful algorithms effectively and correctly interpret the results obtained by them, so as to conduce to the further development not only in the structural prediction area, but in some other relevant areas in protein science as well.