Will diverse Tat interactions lead to novel antiretroviral drug targets?

More than fifteen years following the description of Tat as a critical HIV gene expression regulatory protein, additional roles for Tat in HIV replication have been described, including reverse transcription. Tat achieves function through direct interaction with viral proteins, including reverse transcriptase, and numerous cellular proteins including cyclin T1, RNA polymerase 11, protein kinase R (PKR), p300/CBP, and P/CAF. Despite our advanced knowledge of how Tat operates, this has not yet resulted in the discovery of effective agents capable of targeting various Tat functions. Nevertheless, Tat remains an attractive, virus-specific molecule and detailed understanding of specific protein interaction holds promise for future drug discovery.

The moving targets algorithm for difficult temporal credit assignment problems

The 'moving targets' algorithm for training recurrent networks is reviewed and applied to a task which demonstrates the ability of this algorithm to use distant contextual information. Some practical difficulties are discussed, especially with regard to the minimization process. Results on performance and computational requirements of several different 2nd-order minimization algorithms are presented for moving target problems.

The 'moving targets' training algorithm

A simple method for training the dynamical behavior of a neural network is derived. It is applicable to any training problem in discrete-time networks with arbitrary feedback. The algorithm resembles back-propagation in that an error function is minimized using a gradient-based method, but the optimization is carried out in the hidden part of state space either instead of, or in addition to weight space. Computational results are presented for some simple dynamical training problems, one of which requires response to a signal 100 time steps in the past.