Compressing the free energy range of substructure stabilities in iso-1-cytochrome c.

Evolutionary conservation of substructure architecture between yeast iso-1-cytochrome c and the well-characterized horse cytochrome c is studied with limited proteolysis, the alkaline conformational transition and global unfolding with guanidine-HCl. Mass spectral analysis of limited proteolysis cleavage products for iso-1-cytochrome c show that its least stable substructure is the same as horse cytochrome c. The limited proteolysis data yield a free energy of 3.8 +/- 0.4 kcal mol(-1) to unfold the least stable substructure compared with 5.05 +/- 0.30 kcal mol(-1) for global unfolding of iso-1-cytochrome c. Thus, substructure stabilities of iso-1-cytochrome c span only approximately 1.2 kcal mol(-1) compared with approximately 8 kcal mol(-1) for horse cytochrome c. Consistent with the less cooperative folding thus expected for the horse protein, the guanidine-HCl m-values are approximately 3 kcal mol(-1)M(-1) versus approximately 4.5 kcal mol(-1)M(-1) for horse versus yeast cytochrome c. The tight free energy spacing of the yeast cytochrome c substructures suggests that its folding has more branch points than for horse cytochrome c. Studies on a variant of iso-1-cytochrome c with an H26N mutation indicate that the least and most stable substructures unfold sequentially and the two least stable substructures unfold independently as for horse cytochrome c. Thus, important aspects of the substructure architecture of horse cytochrome c, albeit compressed energetically, are preserved evolutionally in yeast iso-1-cytochrome c.

Identifying topic sentencehood.

Four experiments were conducted to assess two models of topic sentencehood identification: the derived model and the free model. According to the derived model, topic sentences are identified in the context of the paragraph and in terms of how well each sentence in the paragraph captures the paragraph's theme. In contrast, according to the free model, topic sentences can be identified on the basis of sentential features without reference to other sentences in the paragraph (i.e., without context). The results of the experiments suggest that human raters can identify topic sentences both with and without the context of the other sentences in the paragraph. Another goal of this study was to develop computational measures that approximated each of these models. When computational versions were assessed, the results for the free model were promising; however, the derived model results were poor. These results collectively imply that humans' identification of topic sentences in context may rely more heavily on sentential features than on the relationships between sentences in a paragraph.