Serine peptidases (SPs) are responsible for most primary protein digestion in Lepidoptera species. An expansion of the number of genes encoding trypsin and chymotrypsin enzymes and the ability to upregulate the expression of some of these genes in response to peptidase inhibitor (PI) ingestion have been associated with the adaptation of Noctuidae moths to herbivory. To investigate whether these gene family expansion events are common to other Lepidoptera groups, we searched for all genes encoding putative trypsin and chymotrypsin enzymes in 23 publicly available genomes from this taxon. Phylogenetic analysis showed that several gene family expansion events may have occurred in the taxon’s evolutionary history and that these events gave rise to a very diverse group of enzymes, including proteins lacking the canonical SP catalytic triad. The expression profile of these enzymes along the midgut and the secretion mechanisms by which these enzymes enter the luminal content were also analyzed in Spodoptera frugiperda larvae using RNA-seq and proteomics. These results support the proposal of a midgut countercurrent flux responsible for the direction of these proteins to the anterior portion of the midgut and show that these enzymes reach the midgut lumen via both exocytosis and microapocrine secretion mechanisms.

Peptides and proteins are involved in several biological processes at a molecular level. In this context, three-dimensional structure characterization and determination of peptides and proteins have helped researchers unravel the chemical and biological role of these macromolecules. Over 50 years, peptide and protein structures have been determined by experimental methods, including nuclear magnetic resonance (NMR), X-ray crystallography, and cryo-electron microscopy (cryo-EM). Therefore, an increasing number of atomic coordinates for peptides and proteins have been deposited in public databases, thus assisting the development of computational tools for predicting unknown 3D structures. In the last decade, a race for innovative methods has arisen in computational sciences, including more complex biological activity and structure prediction algorithms. As a result, peptide/protein theoretical models have achieved a new level of structure prediction accuracy compared with experimentally determined structures. Machine learning and deep learning approaches, for instance, incorporate fundamental aspects of peptide/protein geometry and include physical/biological knowledge about these macromolecules' experimental structures to build more precise computational models. Additionally, computational strategies have helped structural biology, including comparative, threading, and ab initio modeling and, more recently, prediction tools based on machine learning and deep learning. Bearing this in mind, here we provide a retrospective of protein and peptide structure prediction tools, highlighting their advances and obstacles and how they have assisted researchers in answering crucial biological questions.

Background: The COVID-19 epidemic overloaded the São Paulo metropolitan area (SPMA) health system in 2020. The leading hospitals directed their attention to patients with COVID-19. At the same time, the SPMA Health Secretary decreed social isolation (SI), which compromised the care for cardiovascular diseases (CVD), even though higher cardiovascular events were expected. Methods: This study analyzed mortality from CVD, ischemic heart disease (IHD), and stroke, along with hospital admissions for CVD, IHD, stroke, and SI in the SPMA in 2020. Data regarding hospitalization and mortality from CVD were obtained from the SPMA Health Department, and data regarding SI was obtained from the São Paulo Intelligent Monitoring System. Time-series trends were analyzed by linear regression, as well as comparisons between these trends. Results: there was an inverse correlation between SI and hospitalizations for CVD (R2 = 0.70; p < 0.001), IHD (R2 = 0.70; p < 0.001), and stroke (R2 = 0.39; p < 0.001). The most significant hospitalization reduction was from March to May, when the SI increased from 43.07% to 50.71%. The increase in SI was also associated with a reduction in CVD deaths (R2 = 0.49; p < 0.001), IHD (R2 = 0.50; p < 0.001), and stroke (R2 = 0.26; p < 0.001). Conclusions: Increased social isolation was associated with reduced hospitalizations and deaths from CVD, IHD, and stroke.

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