Ligand Binding Introduces Significant Allosteric Shifts in the Locations of Protein Fluctuations

Allostery is usually considered to be a mechanism for transmission of signals associated with physical or dynamic changes in some part of a protein. Here, we investigate the changes in fluctuations across the protein upon ligand binding based on the fluctuations computed with elastic network models. These results suggest that binding reduces the fluctuations at the binding site but increases fluctuations at remote sites, but not to fully compensating extents. If there were complete conservation of entropy, then only the enthalpies of binding would matter and not the entropies; however this does not appear to be the case. Experimental evidence also suggests that energies and entropies of binding can compensate but that the extent of compensation varies widely from case to case. Our results do however always show transmission of an allosteric signal to distant locations where the fluctuations are increased. These fluctuations could be used to compute entropies to improve evaluations of the thermodynamics of binding. We also show the allosteric relationship between peptide binding in the GroEL trans-ring that leads directly to the release of GroES from the GroEL-GroES cis-ring. This finding provides an example of how calculating these changes to protein dynamics induced by the binding of an allosteric ligand can regulate protein function and mechanism.

Impact of disease information (Ebola and COVID-19) on the pharmaceutical sector in Russia and USA

Objective: identification of the relationship between the news coverage of global diseases and the dynamics of the return on shares of the pharmaceutical sector for Russia and the United States.Material and methods. The empirical base of the study includes more than 700 thousand tweets on Ebola and COVID-19 in Russian and English, news of the RBC news agency. The sentiment of the text was assessed on the basis of five English and four Russian-language dictionaries, the influence of fundamental and textual variables on the profitability of pharmaceutical companies' shares was carried out using the ARMAX-GARCH econometric model.Results. It has been proven that the dynamics of the stock index of pharmaceutical companies is explained by fundamental (economic) and sentimental factors. News of any epidemics negatively affects the pharmaceutical sector in the US and Russia, that is, there are no industries that benefit from this situation. Pandemic news affects US pharmaceutical companies more than Russian companies. The effect of news influence depends on the level of spread of the disease. News influences not only at the moment of their publication, but also after: there is a "delayed effect". Ebola news affects the American pharmaceutical market for 2 weeks, and the dynamics of the increase in influence can be traced. News on the COVID pandemic amplifies its impact during 1 week for the Russian pharmaceutical market and for 2 weeks for the US pharmaceutical companies. As for news sources, the elastic network has identified more significant variables based on publications from RBC; therefore, Internet publications generate more publicity, shaping a more significant overall sentiment in the markets.Conclusion. The models developed in the framework of the study and the economic conclusions obtained have not only theoretical, but also practical significance, and can also be used for further research in this area. It is possible to give recommendations on the practical use of dictionaries to assess the sentiment of the text. In our study, the elastic network method chose the Loughran–McDonald dictionary for evaluating economic texts in English and the EcSentiThemeLex dictionary (designed in R and Python programming environments). Avenues for further investigation may include analysis of other sources of information about the pandemic.

Elastic Network Modeling of Cellular Networks Unveils Sensor and Effector Genes that Control Information Flow

The high-level organization of the cell is embedded in long-range interactions that connect distinct cellular processes. Existing approaches for detecting long-range interactions consist of propagating information from source nodes through cellular networks, but the selection of source nodes is inherently biased by prior knowledge. Here, we sought to derive an unbiased view of long-range interactions by adapting a perturbation-response scanning strategy initially developed for identifying allosteric interactions within proteins. We deployed this strategy onto an elastic network model of the yeast genetic network. The genetic network revealed a superior propensity for long-range interactions relative to simulated networks with similar topology. Long-range interactions were detected systematically throughout the network and found to be enriched in specific biological processes. Furthermore, perturbation-response scanning identified the major sources and receivers of information in the network, named effector and sensor genes, respectively. Effectors formed dense clusters centrally integrated into the network, whereas sensors formed loosely connected antenna-shaped clusters. Long-range interactions between effector and sensor clusters represent the major paths of information in the network. Our results demonstrate that elastic network modeling of cellular networks constitutes a promising strategy to probe the high-level organization of the cell.

Data Mining Analysis of Gene Prognostic Markers of Metastatic Skin Cancer Based on the Elastic Network Method

Skin cancer is a typical cancer tumor, which occurs all over the world and has a relatively high recurrence rate, including metastatic tumors that occur in other tissues and metastases to the skin, thus jeopardizing the personal life satisfaction and soundness of patients. Due to individual differences, the traditional treatment methods cannot adapt to every patient accurately, so it is difficult to achieve the desired treatment effect for each individual. Nowadays, with the development of gene chip, many new therapies based on gene are more targeted and flexible for the treatment of skin cancer patients. Therefore, it is necessary to mine and analyze appropriate gene biomarkers according to patients' genes. Because of the high cost of gene chip technology and the large number of human genes, there are few samples of gene data and high dimensions. It is a key problem to mine effective genetic biomarkers from the sample data. In this paper, we firstly performed the preliminary analysis using the difference expression analysis and proportional hazards model, then used the elastic network method to reduce the range of genetic data selection, and screened 26 gene prognostic markers closely related to the recurrence of metastatic skin cancer. Finally, the 26 gene biomarkers were analyzed by functional analysis and verified using a test sample. Research findings have shown that the obtained genetic markers have certain value in the clinical prognostic treatment of metastatic skin cancer.

ClustENMD: Efficient sampling of biomolecular conformational space at atomic resolution

SummaryEfficient sampling of conformational space is essential for elucidating functional/allosteric mechanisms of proteins and generating ensembles of conformers for docking applications. However, unbiased sampling is still a challenge especially for highly flexible and/or large systems. To address this challenge, we describe the new implementation of our computationally efficient algorithm ClustENMD that is integrated with ProDy and OpenMM softwares. This hybrid method performs iterative cycles of conformer generation using elastic network model (ENM) for deformations along global modes, followed by clustering and short molecular dynamics (MD) simulations. ProDy framework enables full automation and analysis of generated conformers and visualization of their distributions in the essential subspace.Availability and implementationClustENMD is open-source and freely available under MIT License from https://github.com/prody/[email protected] or [email protected] informationSupplementary materials comprise method details, figures, table and tutorial.