PubMed · 12833054
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2003. Editorial procedures reviewed.. https://doi.org/10.1038/ncb0703-583
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In 1978, for my PhD, I developed the efficient O(n3) dynamic programming algorithm for the-then open problem of RNA secondary structure prediction. This algorithm, now dubbed the "Nussinov algorithm", "Nussinov plots", and "Nussinov diagrams", is still taught across Europe and the U.S. As sequences started coming out in the 1980s, I started seeking genome-encoded functional signals, later becoming a bioinformatics trend. In the early 1990s I transited to proteins, co-developing a powerful computer vision-based docking algorithm. In the late 1990s, I proposed the foundational role of conformational ensembles in molecular recognition and allostery. At the time, conformational ensembles and free energy landscapes were viewed as physical properties of proteins but were not associated with function. The classical view of molecular recognition and binding was based on only two conformations captured by crystallography: open and closed. I proposed that all conformational states preexist. Proteins always have not one folded form-nor two-but many folded forms. Thus, rather than inducing fit, binding can work by shifting the ensembles between states, and this shifting, or redistributing the ensembles to maintain equilibrium, is the origin of the allosteric effect and protein, thus cell, function. This transformative paradigm impacted community views in allosteric drug design, catalysis, and regulation. Dynamic conformational ensemble shifts are now acknowledged as the origin of recognition, allostery, and signaling, underscoring that conformational ensembles-not proteins-are the workhorses of the cell, pioneering the fundamental idea that dynamic ensembles are the driving force behind cellular processes. Nussinov was recognized as pioneer in molecular biology by JMB.
BACKGROUND: The differential expression of virulence genes is often used by microbial pathogens in adapting to the environment of their host. The differential expression of such sets of genes can be regulated by RNA polymerase sigma factors. Some sigma factors are differentially expressed, which can provide a means to identifying other differentially expressed genes such as those whose expression are controlled by the sigma factor. METHODS: To identify sigma factor-regulated genes, we developed a method, termed I-TRAP, for the identification of transcriptional regulator activated promoters. The I-TRAP method is based on the fact that some genes will be differentially expressed in the presence and absence of a transcriptional regulator. I-TRAP uses a DNA library in a promoter-trap vector that contains two reporter genes, one to allow the selection of active promoters in the presence of the transcriptional regulator and a second to allow screening for promoter activity in the absence of the transcriptional regulator. RESULTS: To illustrate the development and use of the I-TRAP approach, the construction of the vectors, host strains, and library necessary to identify SigmaE-regulated genes of Mycobacterium tuberculosis is described. CONCLUSION: The I-TRAP method should be a versatile and useful method for identifying and characterizing promoter activity under a variety of conditions and in response to various regulatory proteins. In our study, we isolated 360 clones that may contain plasmids carrying SigmaE-regulated promoters genes of M. tuberculosis.
A response to Life sentences: Ontology recapitulates philology by Sydney Brenner, Genome Biology 2002, 3:comment1006.1-1006.2.