The subunits MECL-1 and LMP2 are mutually required for incorporation into the 20S proteasome

Processing of antigens for presentation by major histocompatibility complex (MHC) class I molecules requires the activity of the proteasome. The 20S proteasome complex is composed of 14 different subunits, 2 of which can be substituted by the interferon γ (IFN-γ)-inducible and MHC-encoded subunits LMP2 and LMP7 (low molecular mass poylpeptides 2 and 7). A third subunit, MECL-1, is inducible by IFN-γ but is encoded outside the MHC. Here we show by cotransfection experiments that the incorporation of MECL-1 into the 20S proteasome is directly dependent on the expression of LMP2 but independent of LMP7. Conversely, the uptake of LMP2 is strongly enhanced by MECL-1 expression. The expression of MECL-1 caused a replacement of the homologous subunit Z in the 20S proteasome complex. LMP2 is required for MECL-1 incorporation at the level of proteasome precursor formation that guarantees the concerted incorporation of two IFN-γ-inducible proteasome subunits encoded inside and outside the MHC. The obligatory coincorporation of MECL-1 and LMP2 is an important parameter for the interpretation of results obtained with LMP2-deficient cell lines and mice as well as for the design of experiments addressing the function of MECL-1 in antigen presentation.

Approximation algorithms

Increasing global competition, rapidly changing markets, and greater consumer awareness have altered the way in which corporations do business. To become more efficient, many industries have sought to model some operational aspects by gigantic optimization problems. It is not atypical to encounter models that capture 106 separate “yes” or “no” decisions to be made. Although one could, in principle, try all 2106 possible solutions to find the optimal one, such a method would be impractically slow. Unfortunately, for most of these models, no algorithms are known that find optimal solutions with reasonable computation times. Typically, industry must rely on solutions of unguaranteed quality that are constructed in an ad hoc manner. Fortunately, for some of these models there are good approximation algorithms: algorithms that produce solutions quickly that are provably close to optimal. Over the past 6 years, there has been a sequence of major breakthroughs in our understanding of the design of approximation algorithms and of limits to obtaining such performance guarantees; this area has been one of the most flourishing areas of discrete mathematics and theoretical computer science.