Frontiers in systems biology最新文献

The COVID-19 pandemic has tested the technical, scientific, and industrial resources of all countries worldwide. Faced with the absence of pharmacological strategies against the disease, an effective plan for vaccinating against SARS-CoV-2 has been essential. Due to the lack of production means and necessary infrastructure, only a few nations could adequately confront this pathogen with a production, storage, and distribution scheme in place. This disease has become endemic in many countries, especially in those that are developing, thus necessitating solutions tailored to their reality. In this paper, we propose an in silico method to guide the design towards a thermally stable, universal, efficient, and safe COVID-19 vaccine candidate against SARS-CoV-2 using bioinformatics, immunoinformatics, and molecular modeling approaches for the selection of antigens with higher immunogenic potential, incorporating them into the surface of the M13 phage. Our work focused on using phagemid display as peptide array for neutralizing antibodies (PdPANA). This alternative approach might be useful during the vaccine development process, since it could bring improvements in terms of cost-effectiveness in production, durability, and ease of distribution of the vaccine under less stringent thermal conditions compared to existing methods. Our results suggest that in the heavily glycosylated region of SARS-CoV-2 Spike protein (aa 344-583), from its inter-glycosylated regions, useful antigenic peptides can be obtained to be used in M13 phagemid display system. PdPANA, our proposed method might be useful to overcome the classic shortcoming posed by the phage-display technique (i.e., the time-consuming task of in vitro screening through great sized libraries with non-useful recombinant proteins) and obtain the most ideal recombinant proteins for vaccine design purposes.

Biocementation is an exciting biomanufacturing alternative to common cement, which is a significant contributor of CO₂ greenhouse gas production. In nature biocementation processes are usually modulated via ureolytic microbes, such as Sporosarcina pasteurii, precipitating calcium carbonate to cement particles together, but these ureolytic reactions also produce ammonium and carbonate byproducts, which may have detrimental effects on the environment. As an alternative approach, this work examines biosilicification via surface-displayed silicatein-α in bio-engineered E. coli as an in vivo biocementation strategy. The surface-display of silicatein-α with ice nucleation protein is a novel protein fusion combination that effectively enables biosilicification, which is the polymerization of silica species in solution, from the surface of E. coli bacterial cells. Biosilicification with silicatein-α produces biocementation products with comparable compressive strength as S. pasteurii. This biosilicification approach takes advantage of the high silica content found naturally in sand and does not produce the ammonium and carbonate byproducts of ureolytic bacteria, making this a more environmentally friendly biocementation strategy.

New discoveries and knowledge are summarized in thousands of published papers per year per scientific domain, making it incomprehensible for scientists to account for all available knowledge relevant for their studies. In this paper, we present ACCORDION (ACCelerating and Optimizing model RecommenDatIONs), a novel methodology and an expert system that retrieves and selects relevant knowledge from literature and databases to recommend models with correct structure and accurate behavior, enabling mechanistic explanations and predictions, and advancing understanding. ACCORDION introduces an approach that integrates knowledge retrieval, graph algorithms, clustering, simulation, and formal analysis. Here, we focus on biological systems, although the proposed methodology is applicable in other domains. We used ACCORDION in nine benchmark case studies and compared its performance with other previously published tools. We show that ACCORDION is: comprehensive, retrieving relevant knowledge from a range of literature sources through machine reading engines; very effective, reducing the error of the initial baseline model by more than 80%, recommending models that closely recapitulate desired behavior, and outperforming previously published tools; selective, recommending only the most relevant, context-specific, and useful subset (15%-20%) of candidate knowledge in literature; diverse, accounting for several distinct criteria to recommend more than one solution, thus enabling alternative explanations or intervention directions.