Understanding the molecular basis of interaction specificity between RGS (regulator of G protein signaling) proteins and heterotrimeric (αβγ) G proteins would enable the manipulation of RGS-G protein interactions, explore their functions, and effectively target them therapeutically. RGS proteins are classified into four subfamilies (R4, R7, RZ, and R12) and function as negative regulators of G protein signaling by inactivating Gα subunits. We found that the R12 subfamily members RGS10 and RGS14 had lower activity than most R4 subfamily members toward the G subfamily member Gα Using structure-based energy calculations with multiple Gα-RGS complexes, we identified R12-specific residues in positions that are predicted to determine the divergent activity of this subfamily. This analysis predicted that these residues, which we call "disruptor residues," interact with the Gα helical domain. We engineered the R12 disruptor residues into the RGS domains of the high-activity R4 subfamily and found that these altered proteins exhibited reduced activity toward Gα Reciprocally, replacing the putative disruptor residues in RGS18 (a member of the R4 subfamily that exhibited low activity toward Gα) with the corresponding residues from a high-activity R4 subfamily RGS protein increased its activity toward Gα Furthermore, the high activity of the R4 subfamily toward Gα was independent of the residues in the homologous positions to the R12 subfamily and RGS18 disruptor residues. Thus, our results suggest that the identified RGS disruptor residues function as negative design elements that attenuate RGS activity for specific Gα proteins.

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There are several factors associated with the sale of cosmetic products which contribute to gaining market share for related companies in this industry. Furthermore, sales forecasting is indispensable in all levels of a company’s supply chain including production, distribution and logistics, marketing, and sale. This article mainly focuses on the analysis of characteristics affecting sales and sales forecasting in the cosmetics industry in which it will be helpful in determining sales strategies of cosmetics companies. Therefore, as a case study in this study, the main factors affecting the sale of cosmetic products were determined and categorized; accordingly. Three products including moisturizing cream, perfume, and sunscreen were examined using a statistical method. The effect of factors on product sales was predicted using the spline smooth prediction method and based on the predicted values, using the non-parametric Friedman test and Mean Rank, the effective factors were ranked in each of the three products. Moreover, the company’s sales volume in each of the three products was forecasted by using ARIMA models. The results demonstrated that factors such as “price” and “product” elements are the main drivers influencing the sales of moisturizing creams and “promotion” and “Inflation rate” factors play the most effective role in the sales of the perfume. Also, the compound aggregated growth rate (CAGR) for moisturizers, perfumes, and sunscreens over a five-year period in the study company are 30%, 29%, and 45%, respectively. It is very clear that to achieve ideal sales, paying attention to these influential factors and forecasting product sales lead to predicting material procurement of manufactures, distribution channels, and sales which finally provides business with customer satisfaction.

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