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Mitoviruses, which are considered evolutionary relics of extinct alpha-proteobacteria RNA phages, represent one of the simplest self-replicating biological systems. This study aims to quantitatively describe genomes and identify potential genomic signatures that support the protein phylogenetic-based classification criterion. Genomic variables, such as mononucleotide and dinucleotide composition, codon usage bias, and minimal free energy derived from optimized predicted RNA secondary structure, were analyzed. From the values obtained, the main evolutionary pressures were discussed, indicating that natural selection plays a significant role in shaping mitovirus genomes. However, neutral evolution also makes a significant contribution. This study reveals a significant discovery of structural divergence in Kvaramitovirus. The energy minimization approach employed to study 2D folding in this study reveals a distinct spatial organization of their genomes, providing evidence for the hypothesis of a single evolutionary event of circularization in the most recent common ancestor of the lineage. This hypothesis was discussed in light of recent discoveries by other researchers that partially support the existence of mitoviruses with circular genomes. Finally, this study represents a significant advancement in the understanding of mitoviruses, as it quantitatively describes the nucleotide sequence at the family and genus taxonomic levels. Additionally, we provide hypotheses that can be experimentally validated to inspire new research and address the gaps in knowledge of this fascinating, basally divergent RNA virus lineage.

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MOTIVATION: Coding and noncoding RNA molecules participate in many important biological processes. Noncoding RNAs fold into well-defined secondary structures to exert their functions. However, the computational prediction of the secondary structure from a raw RNA sequence is a long-standing unsolved problem, which after decades of almost unchanged performance has now re-emerged due to deep learning. Traditional RNA secondary structure prediction algorithms have been mostly based on thermodynamic models and dynamic programming for free energy minimization. More recently deep learning methods have shown competitive performance compared with the classical ones, but there is still a wide margin for improvement. RESULTS: In this work we present sincFold, an end-to-end deep learning approach, that predicts the nucleotides contact matrix using only the RNA sequence as input. The model is based on 1D and 2D residual neural networks that can learn short- and long-range interaction patterns. We show that structures can be accurately predicted with minimal physical assumptions. Extensive experiments were conducted on several benchmark datasets, considering sequence homology and cross-family validation. sincFold was compared with classical methods and recent deep learning models, showing that it can outperform the state-of-the-art methods.

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N-capping (N-cap) and C-capping (C-cap) in biologically active peptides, including specific amino acids or unconventional group motifs, have been shown to modulate activity against pharmacological targets by interfering with the peptide's secondary structure, thus generating unusual scaffolds. The insertion of capping motifs in linear peptides has been shown to prevent peptide degradation by reducing its susceptibility to proteolytic cleavage, and the replacement of some functional groups by unusual groups in N- or C-capping regions in linear peptides has led to optimized peptide variants with improved secondary structure and enhanced activity. Furthermore, some essential amino acid residues that, when placed in antimicrobial peptide (AMP) capping regions, are capable of complexing metals such as Cu2+, Ni2+, and Zn2+, give rise to the family known as metallo-AMPs, which are capable of boosting antimicrobial efficacy, as well as other activities. Therefore, this review presents and discusses the different strategies for creating N- and C-cap motifs in AMPs, aiming at fine-tuning this class of antimicrobials.

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RAMOSA1 (RA1) is a Cys2-His2-type (C2H2) zinc finger transcription factor that controls plant meristem fate and identity and has played an important role in maize domestication. Despite its importance, the origin of RA1 is unknown, and the evolution in plants is only partially understood. In this paper, we present a well-resolved phylogeny based on 73 amino acid sequences from 48 embryophyte species. The recovered tree topology indicates that, during grass evolution, RA1 arose from two consecutive SUPERMAN duplications, resulting in three distinct grass sequence lineages: RA1-like A, RA1-like B, and RA1; however, most of these copies have unknown functions. Our findings indicate that RA1 and RA1-like play roles in the nucleus despite lacking a traditional nuclear localization signal. Here, we report that copies diversified their coding region and, with it, their protein structure, suggesting different patterns of DNA binding and protein-protein interaction. In addition, each of the retained copies diversified regulatory elements along their promoter regions, indicating differences in their upstream regulation. Taken together, the evidence indicates that the RA1 and RA1-like gene families in grasses underwent subfunctionalization and neofunctionalization enabled by gene duplication.

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Recombinant proteins are of great importance in modern society, mostly as biopharmaceutical products. However, challenging and complex processes with low production yield are major drawbacks. Normally, the optimization to overcome these obstacles is focused on bioreactor and purification processes, and the biomolecular aspects are neglected, seen as less important. In this work, we present how the 5′ mRNA secondary structure region can be relevant for translation and, therefore, protein production. For this, Escherichia coli BL21(DE3) clones, producing recombinant detoxified pneumolysin (PdT) with and without the N-terminal His-tag, were cultivated in 10-L bioreactors. Another version of the pdt gene (version 2) with synonymous changes in the 5′-end nucleotide sequence was also obtained. Protein production, plasmid stability, carbon sources, and acetic acid were quantified during the cultures. Furthermore, in silico mRNA analyses were performed using TIsigner and RNAfold. The results showed that the His-tag presence at the N-terminus generated a minimum 1.5-fold increase in target protein synthesis, which was explained by the in silico mRNA analyses that returned an mRNA secondary structure easier to translate and, therefore, higher protein production than without the His-tag. The pdt gene version 2 showed lower 5′ mRNA opening energy than version 1, allowing higher PdT production even without a tag. This work reveals that simple mRNA analyses during heterologous gene design and production steps can help reach high-recombinant protein titers in a shorter time than using only traditional bioprocess optimization strategies.

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The impact of salivary alterations on chickpea protein structure in the elderly has not been well documented. This study aimed to understand the role of simulated salivary alterations in the conformational properties and secondary structure of the chickpea protein isolate (CPI). Whey protein isolate (WPI) was used as the reference. Protein dispersions (10%) were subjected to in vitro oral processing under simulated salivary conditions in both the elderly and adult subjects. Proteins and their oral counterparts were characterized in terms of their composition, charge, size, solubility, water absorption, molecular weight (MW), and secondary structure (Circular Dichroism and Raman spectroscopy). Under condition of simulated oral digestion in the elderly population, the ordered secondary protein structure was significantly affected, decreasing α-helix by ~36% and ~29% in CPI and WPI compared to the control (adult) population, respectively. An increase in the unordered random coil state was observed. These results could be attributed to an increase in electrolytes in the salivary composition. The structure of CPI is more stable than that of WPI because of its higher MW, more rigid structure, less charged surface, and different amino acid compositions. This study is meaningful in understanding how alterations in the elderly oral system affect protein conformation and is expected to improve the understanding of plant-based protein digestibility.

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Apple snails of the genus Pomacea Perry, 1810 (Mollusca: Caenogastropoda: Ampullariidae) are native to the Neotropics and exhibit high species diversity, holding cultural and ecological significance as an important protein source in Peru. However, most genetic studies in Pomacea have focused mostly on invasive species, especially in Southeast Asia, where they are considered important pests. In this study, we assembled and annotated the mitochondrial genomes of two Pomacea species native to the Peruvian Amazon: Pomacea reevei Ampuero & Ramírez, 2023 and Pomacea aulanieri (Deville & Hupé, 1850). The mitogenomes of P. reevei and P. aulanieri comprise 15,660 and 16,096 bp, respectively, and contain the typical 37 genes of the animal mitochondria with a large control region of 292 bp in P. reevei and 524 bp in P. aulanieri-which fall within the range of what is currently known in Pomacea. Comparisons with previously published mitogenomes in Pomacea revealed differences in the overlapping of adjacent genes, the size of certain protein-coding genes (PCGs) and the secondary structure of some tRNAs that are consistent with the phylogenetic relationships between these species. These findings provide valuable insights into the systematics and genomics of the genus Pomacea.

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This study aimed to optimize the 3D printing parameters of salmon gelatin gels (SGG) using artificial neural networks with the genetic algorithm (ANN-GA) and response surface methodology (RSM). In addition, the influence of the optimal parameters obtained using the two different methodologies was evaluated for the physicochemical and digestibility properties of the printed SGG (PSGG). The ANN-GA had a better fit (R2 = 99.98%) with the experimental conditions of the 3D printing process than the RSM (R2 = 93.99%). The extrusion speed was the most influential parameter according to both methodologies. The optimal values of the printing parameters for the SGG were 0.70 mm for the nozzle diameter, 0.5 mm for the nozzle height, and 24 mm/s for the extrusion speed. Gel thermal properties showed that the optimal 3D printing conditions affected denaturation temperature and enthalpy, improving digestibility from 46.93% (SGG) to 51.52% (PSGG). The secondary gel structures showed that the ß-turn structure was the most resistant to enzymatic hydrolysis, while the intermolecular ß-sheet was the most labile. This study validated two optimization methodologies to achieve optimal 3D printing parameters of salmon gelatin gels, with improved physicochemical and digestibility properties for use as transporters to incorporate high value nutrients to the body.

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BACKGROUND: The delimitation of species of Tulasnella has been extensively studied, mainly at the morphological (sexual and asexual states) and molecular levels-showing ambiguity between them. An integrative species concept that includes characteristics such as molecular, ecology, morphology, and other information is crucial for species delimitation in complex groups such as Tulasnella. OBJECTIVES: The aim of this study is to test evolutionary relationships using a combination of alignment-based and alignment-free distance matrices as an alternative molecular tool to traditional methods, and to consider the secondary structures and CBCs from ITS2 (internal transcribed spacer) sequences for species delimitation in Tulasnella. METHODOLOGY: Three phylogenetic approaches were plotted: (i) alignment-based, (ii) alignment-free, and (iii) a combination of both distance matrices using the DISTATIS and pvclust libraries from an R package. Finally, the secondary structure consensus was modeled by Mfold, and a CBC analysis was obtained to complement the species delimitation using 4Sale. RESULTS AND CONCLUSIONS: The phylogenetic tree results showed delimited monophyletic clades in Tulasnella spp., where all 142 Tulasnella sequences were divided into two main clades A and B and assigned to seven species (T. asymmetrica, T. andina, T. eichleriana ECU6, T. eichleriana ECU4 T. pinicola, T. violea), supported by bootstrap values from 72% to 100%. From the 2D secondary structure alignment, three types of consensus models with helices and loops were obtained. Thus, T. albida belongs to type I; T. eichleriana, T. tomaculum, and T. violea belong to type II; and T. asymmetrica, T. andina, T. pinicola, and T. spp. (GER) belong to type III; each type contains four to six domains, with nine CBCs among these that corroborate different species.

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Increasing the repertoire of available complementary tools to advance the knowledge of protein structures is fundamental for structural biology. The Neighbors Influence of Amino Acids and Secondary Structures (NIAS) is a server that analyzes a protein's conformational preferences of amino acids. NIAS is based on the Angle Probability List, representing the normalized frequency of empirical conformational preferences, such as torsion angles, of different amino acid pairs and their corresponding secondary structure information, as available in the Protein Data Bank. In this work, we announce the updated NIAS server with the data comprising all structures deposited until Sep 2022, 7 years after the initial release. Unlike the original publication, which accounted for only studies conducted with X-ray crystallography, we added data from solid nuclear magnetic resonance (NMR), solution NMR, CullPDB, Electron Microscopy, and Electron Crystallography using multiple filtering parameters. We also provide examples of how NIAS can be applied as a complementary analysis tool for different structural biology works and what are its limitations.

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Recombinant proteins are of great importance in modern society, mostly as biopharmaceutical products. However, challenging and complex processes with low production yield are major drawbacks. Normally, the optimization to overcome these obstacles is focused on bioreactor and purification processes, and the biomolecular aspects are neglected, seen as less important. In this work, we present how the 5' mRNA secondary structure region can be relevant for translation and, therefore, protein production. For this, Escherichia coli BL21(DE3) clones, producing recombinant detoxified pneumolysin (PdT) with and without the N-terminal His-tag, were cultivated in 10-L bioreactors. Another version of the pdt gene (version 2) with synonymous changes in the 5'-end nucleotide sequence was also obtained. Protein production, plasmid stability, carbon sources, and acetic acid were quantified during the cultures. Furthermore, in silico mRNA analyses were performed using TIsigner and RNAfold. The results showed that the His-tag presence at the N-terminus generated a minimum 1.5-fold increase in target protein synthesis, which was explained by the in silico mRNA analyses that returned an mRNA secondary structure easier to translate and, therefore, higher protein production than without the His-tag. The pdt gene version 2 showed lower 5' mRNA opening energy than version 1, allowing higher PdT production even without a tag. This work reveals that simple mRNA analyses during heterologous gene design and production steps can help reach high-recombinant protein titers in a shorter time than using only traditional bioprocess optimization strategies.

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Cell-penetrating peptides rich in arginine are good candidates to be considered as antibacterial compounds, since peptides have a lower chance of generating resistance than commonly used antibiotics. Model homopeptides are a useful tool in the study of activity and its correlation with a secondary structure, constituting an initial step in the construction of functional heteropeptides. In this report, the 11-residue arginine homopeptide (R11) was used to determine its antimicrobial activity against Staphylococcus aureus and Escherichia coli and the effect on the secondary structure, caused by the substitution of the arginine residue by the amino acids Ala, Pro, Leu and Trp, using the scanning technique. As a result, most of the substitutions improved the antibacterial activity, and nine peptides were significantly more active than R11 against the two tested bacteria. The cell-penetrating characteristic of the peptides was verified by SYTOX green assay, with no disruption to the bacterial membranes. Regarding the secondary structure in four different media-PBS, TFE, E. coli membrane extracts and DMPG vesicles-the polyproline II structure, the one of the parent R11, was not altered by unique substitutions, although the secondary structure of the peptides was best defined in E. coli membrane extract. This work aimed to shed light on the behavior of the interaction model of penetrating peptides and bacterial membranes to enhance the development of functional heteropeptides.

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MOTIVATION: In contrast to messenger RNAs, the function of the wide range of existing long noncoding RNAs (lncRNAs) largely depends on their structure, which determines interactions with partner molecules. Thus, the determination or prediction of the secondary structure of lncRNAs is critical to uncover their function. Classical approaches for predicting RNA secondary structure have been based on dynamic programming and thermodynamic calculations. In the last 4 years, a growing number of machine learning (ML)-based models, including deep learning (DL), have achieved breakthrough performance in structure prediction of biomolecules such as proteins and have outperformed classical methods in short transcripts folding. Nevertheless, the accurate prediction for lncRNA still remains far from being effectively solved. Notably, the myriad of new proposals has not been systematically and experimentally evaluated. RESULTS: In this work, we compare the performance of the classical methods as well as the most recently proposed approaches for secondary structure prediction of RNA sequences using a unified and consistent experimental setup. We use the publicly available structural profiles for 3023 yeast RNA sequences, and a novel benchmark of well-characterized lncRNA structures from different species. Moreover, we propose a novel metric to assess the predictive performance of methods, exclusively based on the chemical probing data commonly used for profiling RNA structures, avoiding any potential bias incorporated by computational predictions when using dot-bracket references. Our results provide a comprehensive comparative assessment of existing methodologies, and a novel and public benchmark resource to aid in the development and comparison of future approaches. AVAILABILITY: Full source code and benchmark datasets are available at: https://github.com/sinc-lab/lncRNA-folding. CONTACT: lbugnon@sinc.unl.edu.ar.

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Protein secondary structures are important in many biological processes and applications. Due to advances in sequencing methods, there are many proteins sequenced, but fewer proteins with secondary structures defined by laboratory methods. With the development of computer technology, computational methods have (started to) become the most important methodologies for predicting secondary structures. We evaluated two different approaches to this problem-driven by the recent results obtained by computational methods in this task-(i) template-free classifiers, based on machine learning techniques; and (ii) template-based classifiers, based on searching tools. Both approaches are formed by different sub-classifiers-six for template-free and two for template-based, each with a specific view of the protein. Our results show that these ensembles improve the results of each approach individually.

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The Honduran white bat, Ectophylla alba (Allen 1982), is one of eight species belonging to the family Phyllostomidae that exclusively roosts in tents. Due to its restricted distribution, habitat specificity, and diet requirements, E. alba has been strongly affected by habitat loss and fragmentation during the last decade. In this study, we developed the first genomic resource for this species; we assembled and analyzed in detail the complete mitochondrial genome of E. alba. The mitogenome of E. alba is 16,664 bp in length and is comprised of 13 protein coding genes (PCGs), 2 ribosomal RNA genes, 22 transfer RNA genes (tRNAs), and a putative Control Region (CR) 1,232 bp in length. Gene arrangement in the mitochondrial chromosome of E. alba is identical to that reported before in other species of co-familiar bats. All PCGs are under purifying selection, with atp8 experiencing the least selective pressure. In all PCGs, codons ending with adenine are preferred over others ending in thymine and cytosine. Except tRNA-Serine 1, all tRNAs exhibit a cloverleaf secondary structure. The CR of E. alba exhibits three domains commonly described in other mammals, including bats; extended terminal associated sequences (ETAS), central, and conserved sequence block (CSB). A ML phylogenetic reconstruction of the family Phyllostomidae based on all 13 mitochondrial PCGs confirms the monophyletic status of the subfamily Sternodermatinae and indicates the close relationship between E. alba and the genus Artibeus. This is the first genomic resource developed for E. alba and represents the first step to improving our understanding of the genomic underpinnings involved in the evolution of specialization as well as acclimatization and adaptation to local and global change of specialist bats.

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Using LAURDAN fluorescence we observed that water dynamics measured at the interface of DOPC bilayers can be differentially regulated by the presence of crowded suspensions of different proteins (HSA, IgG, Gelatin) and PEG, under conditions where the polymers are not in direct molecular contact with the lipid interface. Specifically, we found that the decrease in water dipolar relaxation at the membrane interface correlates with an increased fraction of randomly oriented (or random coil) configurations in the polymers, as Gelatin > PEG > IgG > HSA. By using the same experimental strategy, we also demonstrated that structural transitions from globular to extended conformations in proteins can induce transitions between lamellar and non-lamellar phases in mixtures of DOPC and monoolein. Independent experiments using Raman spectroscopy showed that aqueous suspensions of polymers exhibiting high proportions of randomly oriented conformations display increased fractions of tetracoordinated water, a configuration that is dominant in ice. This indicates a greater capacity of this type of structure for polarizing water and consequently reducing its chemical activity. This effect is in line with one of the tenets of the Association Induction Hypothesis, which predicts a long-range dynamic structuring of water molecules via their interactions with proteins (or other polymers) showing extended conformations. Overall, our results suggest a crucial role of water in promoting couplings between structural changes in macromolecules and supramolecular arrangements of lipids. This mechanism may be of relevance to cell structure/function when the crowded nature of the intracellular milieu is considered.

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Three-dimensional RNA domain reconstruction is important for the assembly, disassembly and delivery functionalities of a packed proteinaceus capsid. However, to date, the self-association of RNA molecules is still an open problem. Recent chemical probing reports provide, with high reliability, the secondary structure of diverse RNA ensembles, such as those of viral genomes. Here, we present a method for reconstructing the complete 3D structure of RNA genomes, which combines a coarse-grained model with a subdomain composition scheme to obtain the entire genome inside proteinaceus capsids based on secondary structures from experimental techniques. Despite the amount of sampling involved in the folded and also unfolded RNA molecules, advanced microscope techniques can provide points of anchoring, which enhance our model to include interactions between capsid pentamers and RNA subdomains. To test our method, we tackle the satellite tobacco mosaic virus (STMV) genome, which has been widely studied by both experimental and computational communities. We provide not only a methodology to structurally analyze the tertiary conformations of the RNA genome inside capsids, but a flexible platform that allows the easy implementation of features/descriptors coming from both theoretical and experimental approaches.

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The influence of the pasta preparation stages on starch, proteins, and water structures of semolina and chickpea pasta was studied. The hydrated starch structures (995/1022 FTIR ratio) increased in semolina and reduced in chickpea pasta. The processing stages in semolina pasta led to a significant increase of ß-sheet structures (~50% to ~68%). The ß-sheet structures content in chickpea pasta was lower (~52%), and was most affected by sheeting and cooking. The water structure was assessed by the analysis of the OH fingerprint FTIR region (3700-2800 cm-1) and showing that water molecules (~90%) are strongly and moderately bound. The chickpea pasta displayed the highest content of strongly bonded water (about 55%) in contrast to the semolina pasta (~48%). A principal component analysis showed that the molecular organization of semolina pasta was mostly affected by dough formation and cooking; the molecular organization of chickpea pasta was determined by the cooking stage.

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BACKGROUND: With more than 300 million potentially infected people every year, and with the expanded habitat of mosquitoes due to climate change, Dengue virus (DENV) cannot be considered anymore only a tropical disease. The RNA secondary structure is a functional characteristic of RNA viruses, and together with the accumulated high-throughput sequencing data could provide general insights towards understanding virus biology. Here, we profiled the RNA secondary structure of > 7000 complete viral genomes from 11 different species focusing on viral hemorrhagic fevers, including DENV serotypes, EBOV, and YFV. RESULTS: In our work we demonstrated that the secondary structure and presence of protein-binding domains in the genomes can be used as intrinsic signature to further classify the viruses. With our predictive approach, we achieved high prediction scores of the secondary structure (AUC up to 0.85 with experimental data), and computed consensus secondary structure profiles using hundreds of in silico models. We observed that viruses show different structural patterns, where e.g., DENV-2 and Ebola virus tend to be less structured than the other viruses. Furthermore, we observed virus-specific correlations between secondary structure and the number of interaction sites with human proteins, reaching a correlation of 0.89 in the case of Zika virus. We also identified that helicases-encoding regions are more structured in several flaviviruses, while the regions encoding for the contact proteins exhibit virus-specific clusters in terms of RNA structure and potential protein-RNA interactions. We also used structural data to study the geographical distribution of DENV, finding a significant difference between DENV-3 from Asia and South-America, where the structure is also driving the clustering more than sequence identity, which could imply different evolutionary routes of this subtype. CONCLUSIONS: Our massive computational analysis provided novel results regarding the secondary structure and the interaction with human proteins, not only for DENV serotypes, but also for other flaviviruses and viral hemorrhagic fevers-associated viruses. We showed how the RNA secondary structure can be used to categorise viruses, and even to further classify them based on the interaction with proteins. We envision that these approaches can be used to further classify and characterise these complex viruses.

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