Dynamic cross correlation analysis of Thermus thermophilus alkaline phosphatase and determinants of thermostability

Soil phosphorus transformation and plant uptake driven by phosphate-solubilizing microorganisms

Phosphorus (P) is an important nutrient for plants, and a lack of available P greatly limits plant growth and development. Phosphate-solubilizing microorganisms (PSMs) significantly enhance the ability of plants to absorb and utilize P, which is important for improving plant nutrient turnover and yield. This article summarizes and analyzes how PSMs promote the absorption and utilization of P nutrients by plants from four perspectives: the types and functions of PSMs, phosphate-solubilizing mechanisms, main functional genes, and the impact of complex inoculation of PSMs on plant P acquisition. This article reviews the physiological and molecular mechanisms of phosphorus solubilization and growth promotion by PSMs, with a focus on analyzing the impact of PSMs on soil microbial communities and its interaction with root exudates. In order to better understand the ability of PSMs and their role in soil P transformation and to provide prospects for research on PSMs promoting plant P absorption. PSMs mainly activate insoluble P through the secretion of organic acids, phosphatase production, and mycorrhizal symbiosis, mycorrhizal symbiosis indirectly activates P via carbon exchange. PSMs can secrete organic acids and produce phosphatase, which plays a crucial role in soil P cycling, and related genes are involved in regulating the P-solubilization ability. This article reviews the mechanisms by which microorganisms promote plant uptake of soil P, which is of great significance for a deeper understanding of PSM-mediated soil P cycling, plant P uptake and utilization, and for improving the efficiency of P utilization in agriculture.

Phosphate-solubilizing bacteria: Their agroecological function and optimistic application for enhancing agro-productivity

Phosphorus (P) is a limiting nutrient in the soil-plant nutrient cycling. Although the exogenous application of chemical P fertilizers can satisfy crop P requirements during critical growth phases. While excessive P fertilizers use results in low phosphorus acquisition efficiency (PAE), it has serious environmental consequences and hastens the depletion of P mineral reserves. Phosphate-solubilizing bacteria (PSB) have the potential to make insoluble phosphate available to plants through solubilization and mineralization, increasing crop yields while maintaining environmental sustainability. Existing reviews mainly focus on the beneficial effects of PSB on crop performance and related mechanisms, while few of them elucidate the action mechanisms of PSB in soil-microbe-plant interactions for crop cultivation with high yield efficiency. Hence, this study provides a comprehensive review of the physicochemical and molecular mechanisms (e.g., root exudates, extracellular polysaccharides, organic acids, phosphatases, and phosphate-specific transport systems) of PSB to facilitate the P cycle in the soil-plant systems. Further, the potential of commercial applications of PSB (e.g., genetic engineering, seed priming and coating) are also discussed in order to highlight their contribution to sustainable agriculture. Finally, existing challenges and future prospects in agricultural applications are proposed. In conclusion, we firmly believe that PSB represent a highly significant biotechnological tool for enhancing agricultural productivity and offers a wide range of extensive potential applications.

Modeling the role of charged residues in thermophilic proteins by rotamer and dynamic cross correlation analysis

Discerning the determinants of protein thermostability is very important both from the theoretical and applied perspective. Different lines of evidence seem to indicate that a dynamical network of salt bridges/charged residues plays a fundamental role in the thermostability of enzymes. In this work, we applied measures of dynamic variance, like the Gini coefficients, Kullback-Leibler (KL) divergence and dynamic cross correlation (DCC) coefficients to compare the behavior of 3 pairs of homologous proteins from the thermophilic bacterium Thermus thermophilus and mesophilic Escherichia coli. Molecular dynamic (MD) simulations of these proteins were performed at 303 K and 363 K. In the characterization of their side chain rotamer distributions, the corresponding Gini coefficients and KL-divergence both revealed significant correlations with temperature. Similarly, a DCC analysis revealed a higher trend to de-correlate the movement of charged residues at higher temperatures in the thermophilic proteins, when compared with their mesophilic homologues. These results highlight the importance of dynamic electrostatic network interactions for the thermostability of enzymes.

A proteomics-MM/PBSA dual approach for the analysis of SARS-CoV-2 main protease substrate peptide specificity

The main protease M^pro of SARS-CoV-2 is a well-studied major drug target. Additionally, it has been linked to this virus' pathogenicity, possibly through off-target effects. It is also an interesting diagnostic target. To obtain more data on possible substrates as well as to assess the enzyme's primary specificity a two-step approach was introduced. First, Terminal Amine Isobaric Labeling of Substrates (TAILS) was employed to identify novel M^pro cleavage sites in a mouse lung proteome library. In a second step, using a structural homology model, the MM/PBSA variant MM/GBSA (Molecular Mechanics Poisson-Boltzmann/Generalized Born Surface Area) free binding energy calculations were carried out to determine relevant interacting amino acids. As a result, 58 unique cleavage sites were detected, including six that displayed glutamine at the P1 position. Furthermore, modeling results indicated that M^pro has a far higher potential promiscuity towards substrates than expected. The combination of proteomics and MM/PBSA modeling analysis can thus be useful for elucidating the specificity of M^pro, and thus open novel perspectives for the development of future peptidomimetic drugs against COVID-19, as well as diagnostic tools.

Dissecting mutational allosteric effects in alkaline phosphatases associated with different Hypophosphatasia phenotypes: An integrative computational investigation

Hypophosphatasia (HPP) is a rare inherited disorder characterized by defective bone mineralization and is highly variable in its clinical phenotype. The disease occurs due to various loss-of-function mutations in ALPL, the gene encoding tissue-nonspecific alkaline phosphatase (TNSALP). In this work, a data-driven and biophysics-based approach is proposed for the large-scale analysis of ALPL mutations-from nonpathogenic to severe HPPs. By using a pipeline of synergistic approaches including sequence-structure analysis, network modeling, elastic network models and atomistic simulations, we characterized allosteric signatures and effects of the ALPL mutations on protein dynamics and function. Statistical analysis of molecular features computed for the ALPL mutations showed a significant difference between the control, mild and severe HPP phenotypes. Molecular dynamics simulations coupled with protein structure network analysis were employed to analyze the effect of single-residue variation on conformational dynamics of TNSALP dimers, and the developed machine learning model suggested that the topological network parameters could serve as a robust indicator of severe mutations. The results indicated that the severity of disease-associated mutations is often linked with mutation-induced modulation of allosteric communications in the protein. This study suggested that ALPL mutations associated with mild and more severe HPPs can exert markedly distinct effects on the protein stability and long-range network communications. By linking the disease phenotypes with dynamic and allosteric molecular signatures, the proposed integrative computational approach enabled to characterize and quantify the allosteric effects of ALPL mutations and role of allostery in the pathogenesis of HPPs.

MDM-TASK-web: MD-TASK and MODE-TASK web server for analyzing protein dynamics

The web server, MDM-TASK-web, combines the MD-TASK and MODE-TASK software suites, which are aimed at the coarse-grained analysis of static and all-atom MD-simulated proteins, using a variety of non-conventional approaches, such as dynamic residue network analysis, perturbation-response scanning, dynamic cross-correlation, essential dynamics and normal mode analysis. Altogether, these tools allow for the exploration of protein dynamics at various levels of detail, spanning single residue perturbations and weighted contact network representations, to global residue centrality measurements and the investigation of global protein motion. Typically, following molecular dynamic simulations designed to investigate intrinsic and extrinsic protein perturbations (for instance induced by allosteric and orthosteric ligands, protein binding, temperature, pH and mutations), this selection of tools can be used to further describe protein dynamics. This may lead to the discovery of key residues involved in biological processes, such as drug resistance. The server simplifies the set-up required for running these tools and visualizing their results. Several scripts from the tool suites were updated and new ones were also added and integrated with 2D/3D visualization via the web interface. An embedded work-flow, integrated documentation and visualization tools shorten the number of steps to follow, starting from calculations to result visualization. The Django-powered web server (available at https://mdmtaskweb.rubi.ru.ac.za/) is compatible with all major web browsers. All scripts implemented in the web platform are freely available at https://github.com/RUBi-ZA/MD-TASK/tree/mdm-task-web and https://github.com/RUBi-ZA/MODE-TASK/tree/mdm-task-web.