Probing the Mutational Interplay between Primary and Promiscuous Protein Functions: A Computational-Experimental Approach

Protein promiscuity is of considerable interest due its role in adaptive metabolic plasticity, its fundamental connection with molecular evolution and also because of its biotechnological applications. Current views on the relation between primary and promiscuous protein activities stem largely from laboratory evolution experiments aimed at increasing promiscuous activity levels. Here, on the other hand, we attempt to assess the main features of the simultaneous modulation of the primary and promiscuous functions during the course of natural evolution. The computational/experimental approach we propose for this task involves the following steps: a function-targeted, statistical coupling analysis of evolutionary data is used to determine a set of positions likely linked to the recruitment of a promiscuous activity for a new function; a combinatorial library of mutations on this set of positions is prepared and screened for both, the primary and the promiscuous activities; a partial-least-squares reconstruction of the full combinatorial space is carried out; finally, an approximation to the Pareto set of variants with optimal primary/promiscuous activities is derived. Application of the approach to the emergence of folding catalysis in thioredoxin scaffolds reveals an unanticipated scenario: diverse patterns of primary/promiscuous activity modulation are possible, including a moderate (but likely significant in a biological context) simultaneous enhancement of both activities. We show that this scenario can be most simply explained on the basis of the conformational diversity hypothesis, although alternative interpretations cannot be ruled out. Overall, the results reported may help clarify the mechanisms of the evolution of new functions. From a different viewpoint, the partial-least-squares-reconstruction/Pareto-set-prediction approach we have introduced provides the computational basis for an efficient directed-evolution protocol aimed at the simultaneous enhancement of several protein features and should therefore open new possibilities in the engineering of multi-functional enzymes

Novel Role of Phosphorylation-Dependent Interaction between FtsZ and FipA in Mycobacterial Cell Division

The bacterial divisome is a multiprotein complex. Specific protein-protein interactions specify whether cell division occurs optimally, or whether division is arrested. Little is known about these protein-protein interactions and their regulation in mycobacteria. We have investigated the interrelationship between the products of the Mycobacterium tuberculosis gene cluster Rv0014c-Rv0019c, namely PknA (encoded by Rv0014c) and FtsZ-interacting protein A, FipA (encoded by Rv0019c) and the products of the division cell wall (dcw) cluster, namely FtsZ and FtsQ. M. smegmatis strains depleted in components of the two gene clusters have been complemented with orthologs of the respective genes of M. tuberculosis. Here we identify FipA as an interacting partner of FtsZ and FtsQ and establish that PknA-dependent phosphorylation of FipA on T77 and FtsZ on T343 is required for cell division under oxidative stress. A fipA knockout strain of M. smegmatis is less capable of withstanding oxidative stress than the wild type and showed elongation of cells due to a defect in septum formation. Localization of FtsQ, FtsZ and FipA at mid-cell was also compromised. Growth and survival defects under oxidative stress could be functionally complemented by fipA of M. tuberculosis but not its T77A mutant. Merodiploid strains of M. smegmatis expressing the FtsZ(T343A) showed inhibition of FtsZ-FipA interaction and Z ring formation under oxidative stress. Knockdown of FipA led to elongation of M. tuberculosis cells grown in macrophages and reduced intramacrophage growth. These data reveal a novel role of phosphorylation-dependent protein-protein interactions involving FipA, in the sustenance of mycobacterial cell division under oxidative stress

Mutability and Importance of a Hypermutable Cell Subpopulation that Produces Stress-Induced Mutants in Escherichia coli

In bacterial, yeast, and human cells, stress-induced mutation mechanisms are induced in growth-limiting environments and produce non-adaptive and adaptive mutations. These mechanisms may accelerate evolution specifically when cells are maladapted to their environments, i.e., when they are are stressed. One mechanism of stress-induced mutagenesis in Escherichia coli occurs by error-prone DNA double-strand break (DSB) repair. This mechanism was linked previously to a differentiated subpopulation of cells with a transiently elevated mutation rate, a hypermutable cell subpopulation (HMS). The HMS could be important, producing essentially all stress-induced mutants. Alternatively, the HMS was proposed to produce only a minority of stress-induced mutants, i.e., it was proposed to be peripheral. We characterize three aspects of the HMS. First, using improved mutation-detection methods, we estimate the number of mutations per genome of HMS-derived cells and find that it is compatible with fitness after the HMS state. This implies that these mutants are not necessarily an evolutionary dead end, and could contribute to adaptive evolution. Second, we show that stress-induced Lac+ mutants, with and without evidence of descent from the HMS, have similar Lac+ mutation sequences. This provides evidence that HMS-descended and most stress-induced mutants form via a common mechanism. Third, mutation-stimulating DSBs introduced via I-SceI endonuclease in vivo do not promote Lac+ mutation independently of the HMS. This and the previous finding support the hypothesis that the HMS underlies most stress-induced mutants, not just a minority of them, i.e., it is important. We consider a model in which HMS differentiation is controlled by stress responses. Differentiation of an HMS potentially limits the risks of mutagenesis in cell clones

Hardware (DNA) circuits

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An algorithm for targeted convergence of Euler or Newton iterations

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Biological feedback

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