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Proteolytic enzymes are crucial for a variety of biological processes in organisms ranging from lower (virus, bacteria, and parasite) to the higher organisms (mammals). Proteases cleave proteins into smaller fragments by catalyzing peptide bonds hydrolysis. Proteases are classified according to their catalytic site, and distributed into four major classes: cysteine proteases, serine proteases, aspartic proteases, and metalloproteases. This review will cover only cysteine proteases, papain family enzymes which are involved in multiple functions such as extracellular matrix turnover, antigen presentation, processing events, digestion, immune invasion, hemoglobin hydrolysis, parasite invasion, parasite egress, and processing surface proteins. Therefore, they are promising drug targets for various diseases. For preventing unwanted digestion, cysteine proteases are synthesized as zymogens, and contain a prodomain (regulatory) and a mature domain (catalytic). The prodomain acts as an endogenous inhibitor of the mature enzyme. For activation of the mature enzyme, removal of the prodomain is necessary and achieved by different modes. The pro-mature domain interaction can be categorized as protein-protein interactions (PPIs) and may be targeted in a range of diseases. Cysteine protease inhibitors are available that can block the active site but no such inhibitor available yet that can be targeted to block the pro-mature domain interactions and prevent it activation. This review specifically highlights the modes of activation (processing) of papain family enzymes, which involve auto-activation, trans-activation and also clarifies the future aspects of targeting PPIs to prevent the activation of cysteine proteases.

The self‐assembling preparation accompanied with template auto‐catalysis loop and the ability to gather energy, induces the appearance of chirality and entropy reduction in biotic systems. However, an abiotic system with biotic characteristics is of great significance but still missing. Here, it is demonstrated that the molecular evolution is characteristic of ultralong carbon nanotube preparation, revealing the advantage of chiral assembly through template auto‐catalysis growth, stepwise‐enriched chirality distribution with decreasing entropy, and environmental effects on the evolutionary growth. Specifically, the defective and metallic nanotubes perform inferiority to semiconducting counterparts, among of which the ones with double walls and specific chirality (n, m) are more predominant due to molecular coevolution. An explicit evolutionary trend for tailoring certain layer chirality is presented toward perfect near‐(2n, n)‐containing semiconducting double‐walled nanotubes. These findings extend our conceptual understanding for the template auto‐catalysis assembly of abiotic carbon nanotubes, and provide an inspiration for preparing chiral materials with kinetic stability by evolutionary growth. An abiotic system with evolutionary characteristics is extraordinary. Herein, the molecular evolution in the growth of ultralong carbon nanotubes is demonstrated. Specific CNTs are revealed to be extremely distinctive in the evolutionary sense, though merely owning delicate structural difference with others, especially, an explicit trend toward perfect near‐(2n, n)‐containing semiconducting double‐walled nanotubes indicates the effect of coevolution.

Concentration gradients provide spatial information for tissue patterning and cell organization, and their robustness under natural fluctuations is an evolutionary advantage. In rod‐shaped Schizosaccharomyces pombe cells, the DYRK‐family kinase Pom1 gradients control cell division timing and placement. Upon dephosphorylation by a Tea4‐phosphatase complex, Pom1 associates with the plasma membrane at cell poles, where it diffuses and detaches upon auto‐phosphorylation. Here, we demonstrate that Pom1 auto‐phosphorylates intermolecularly, both in vitro and in vivo , which confers robustness to the gradient. Quantitative imaging reveals this robustness through two system's properties: The Pom1 gradient amplitude is inversely correlated with its decay length and is buffered against fluctuations in Tea4 levels. A theoretical model of Pom1 gradient formation through intermolecular auto‐phosphorylation predicts both properties qualitatively and quantitatively. This provides a telling example where gradient robustness through super‐linear decay, a principle hypothesized a decade ago, is achieved through autocatalysis. Concentration‐dependent autocatalysis may be a widely used simple feedback to buffer biological activities. Synopsis Theoretical modeling and experimental data show that the DYRK‐family kinase Pom1 auto‐phosphorylates intermolecularly. This mechanism confers robustness to Pom1 concentration gradients through super‐linear decay. The DYRK‐family kinase Pom1 auto‐phosphorylates intermolecularly in vivo and in vitro . Quantitative imaging of Pom1 gradient shows gradient robustness through two system's level properties. A theoretical model of Pom1 gradient formation through intermolecular auto‐phosphorylation predicts these properties qualitatively and quantitatively. Thus, Pom1 gradients provide an example of gradient robustness through super‐linear decay. Graphical Abstract Theoretical modeling and experimental data show that the DYRK‐family kinase Pom1 auto‐phosphorylates intermolecularly. This mechanism confers robustness to Pom1 concentration gradients through super‐linear decay.

Computational modeling and the theory of nonlinear dynamical systems allow us not only to simply describe the events of biochemical oscillators in the ethanol fermentation process but also to understand why these events occur. This article reviews results of experimental and theoretical studies about the behavior of fermentation systems for bio-ethanol production so as to understand the self-oscillatory phenomena that could affect productivity in industry. In general, Hopf bifurcation and limit cycles are the theoretical basis for the oscillations observed in continuous ethanol fermentation processes, but the underline mechanisms and causes might be different because the studied system is a collection of multi-scale oscillators. To characterize the oscillatory dynamics quantitatively, negative feedback laws are implemented. However, the stimulated oscillation through linear feedback is not adequate in describing such complex dynamics. Hence, elements of nonlinearity, auto-catalysis, and time delay are sorted out and added into the feedback loops to formulate biochemical oscillators. Then, we discuss specific examples of the various models and classify them according to the three kinds of mechanisms: nonlinear feedback, positive feedback, and delay feedback. These mechanisms and modeling work might be used as a guide for process design/operation to eliminate possible oscillations and to develop out advanced configurations that could produce bio-ethanol in a continuous, cost-effective manner.