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.

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.

Background/Objectives: The rise in cancer cases is a major global public health problem. Treating the disease and maintaining health generate high economic and social costs. In recent years, treatment resistance has become a challenge, reinforcing the need for new anticancer agents. In this context, anticancer peptides (ACPs) stand out for their selective interaction with cancer cell membranes. This study aimed to evaluate the activity of the RQ18 peptide against cancer cells. Methods: The cytotoxicity of RQ18 was determined by the MTT assay in murine melanoma (B16F10-Nex2), human melanoma (Sk-mel-28 and A375), breast cancer (MCF-7), cervical cancer (HeLa), murine macrophage (RAW 264.7), and human fibroblast (FN1). Caspase-3/7 activity was further evaluated in B16F10-Nex2, Sk-mel-28, and A375 melanoma cells to investigate the involvement of apoptotic pathways. In addition, intracellular oxidative stress was assessed using the DCFH-DA probe in these three melanoma cell lines. In B16F10-Nex2 cells, cellular morphological changes were examined by microscopy, while apoptosis-associated events were characterized using the markers MitoTracker Deep Red, and NucBlue, complemented by flow cytometry analyses. In addition, molecular dynamics simulations were employed to investigate its interactions with lipid membranes. Results: The peptide exhibited anticancer activity, with IC₅₀ values of 4.4, 11.58 and 59.99 µM·mL⁻¹, against cells B16F10-Nex2, Sk-mel-28 and A375, respectively. Compared with non-cancerous cells, RQ18 preferentially affected cancer cells, especially B16F10-Nex2. In contrast, the peptide was nontoxic to healthy cells, thus indicating selectivity for cancer cells. Morphological analysis revealed alterations compatible with apoptosis, including cell rounding and chromatin condensation. RQ18 increased caspase-3/7 activity in B16F10-Nex2, Sk-mel-28, and A375 cells and induced intracellular oxidative stress, as indicated by increased DCFH-DA fluorescence. Flow cytometry data confirmed the induction of apoptotic cell death in B16F10-Nex2 cells. Caspase activation, detected by a caspase-3/7 assay in three melanoma cell lines, together with flow cytometry data, supported the involvement of apoptotic cell death. Molecular dynamics simulations suggest that the peptide interacts stably with cancer cell-like membranes and retains an α-helical conformation during membrane association. Conclusions: In summary, the RQ18 peptide demonstrates preferential anticancer activity, particularly against melanoma cells, supporting further investigation of its therapeutic potential.

Flies maggots are used to estimate the postmortem interval (PMI) through their developmental time in forensic entomology. Maggots identification is hard since they often have similar morphologies. Computer vision techniques and machine learning seem to be a good alternative to solve this problem. The aim is to create maggots microscopic images database and apply the dataset with an algorithm to automate the maggots identification. Although that approach could be used in forensic entomological identification and criminal expertise, this paper focuses on comparing the image classifications with IBK, J48, Random Forest and Random Tree classifiers. The Random Forest algorithm achieved the best performance, which was above 80% in most tests using the precision metric (P).