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Lysine crotonylation is a newly discovered and reversible posttranslational modification involved in various biological processes, especially metabolism regulation. A total of 5159 lysine crotonylation sites in 2272 protein groups were identified. Twenty-seven motifs were found to be the preferred amino acid sequences for crotonylation sites. Functional annotation analyses revealed that most crotonylated proteins play important roles in metabolic processes and photosynthesis. Bioinformatics analysis suggested that lysine crotonylation preferentially targets a variety of important biological processes, including ribosome, glyoxylate and dicarboxylate metabolism, carbon fixation in photosynthetic organisms, proteasome and the TCA cycle, indicating lysine crotonylation is involved in the common mechanism of metabolic regulation. A protein interaction network analysis revealed that diverse interactions are modulated by protein crotonylation. These results suggest that lysine crotonylation is involved in a variety of biological processes. HSP70 is a crucial protein involved in protecting plant cells and tissues from thermal or abiotic stress responses, and HSP70 protein was found to be crotonylated in paper mulberry. This systematic analysis provides the first comprehensive analysis of lysine crotonylation in paper mulberry and provides important resources for further study on the regulatory mechanism and function of the lysine crotonylated proteome. [ABSTRACT FROM AUTHOR]

This correction notice addresses errors in a scientific paper titled "A Cell-Free SDKP-Conjugated Self-Assembling Peptide Hydrogel Sufficient for Improvement of Myocardial Infarction." The authors discovered that some representative images in Figures 4 and 5 overlapped with images from another published paper. This occurred because the experiments were performed simultaneously. The authors apologize for any inconvenience caused and state that the scientific conclusions were unaffected. The corrected version of the paper will be updated on the article webpage. Additionally, a correction was made to the peptide sequence in the Materials and Methods section due to intellectual property issues. The paper includes graphs showing the effects of the hydrogel on scar size, angiogenesis, and inflammation. [Extracted from the article]

The process of synthesizing radionuclide-coupled drugs, especially shutdown technology that links bipotent chelators with biomolecules, utilizes traditional coupling reactions, including emerging click chemistry; these reactions involve different drawbacks, such as complex and cumbersome reaction steps, long reaction times, and the use of catalysts at various pH values, which can negatively impact the effects of the chelating agent. To address the above problems in this study, This research designed a novel bipotent chelator coupled with peptides. In the present study, dichloromethane was used as a solvent, and the reaction was conducted at room temperature for 12 h. A one-step ring-opening method was employed to introduce the coupling functional group of tridentate amide acid. The coupling materials consisted of the amino active site of the peptide and diethylene glycol anhydride. In this paper, this study explored the reactions between different equivalents of acid anhydride coupled to the peptide (peptide sequence: HLRKLRKR) and determined that the maximum conversion of the peptide feedstock was 87%. To determine the selectivity of the reaction sites in this polypeptide, This study identified the peptide sequence at the reaction site using nuclear magnetic resonance (NMR) and liquid chromatography–mass spectrometry (LC-MS). For the selected peptide, the first reactive site was on the terminal amino group, followed by the amino group on the tetra- and hepta-lysine side chains. The tridentate amic acid framework functions as a chelating agent, capable of binding a range of lanthanide ions. This significantly reduces and optimizes the time and cost associated with synthesizing radionuclide-coupled drugs. [ABSTRACT FROM AUTHOR]

The average content of casein in yak milk is 40.2 g/L. Casein can be degraded by enzymatic digestion or food processing to produce abundant degradation peptides. International researchers have studied the degradation peptides of yak milk casein by using multiple techniques and methods, such as in vitro activity tests, cellular experiments, proteomics, bioinformatics, etc., and found that the degradation peptides have a wide range of functional activities that are beneficial to the human body, such as angiotensin-converting enzyme (ACE) inhibitory, antioxidant, anti-inflammatory, antidiabetic, antimicrobial, anticancer, and immunomodulatory activities, etc., and it has been proved that the types and strengths of functional activities are closely related to the structural characteristics of the peptides. This paper describes the characteristics of yak milk proteins, the functional activities, and mechanism of action of degraded peptides. Based on the types of functional activities of yak milk casein degradation peptides, we classified and elucidated the effects of structural factors, such as peptide molecular weight, peptide length, amino acid sequence, physicochemical properties, electrical charge, hydrophobicity, spatial conformation, chain length, and the type of enzyme on these activities. It reveals the great potential of yak milk casein degradation peptides as functional active peptide resources and as auxiliary treatments for diseases. It also provides important insights for analyzing yak casein degradation peptide activity and exploring high-value utilization. [ABSTRACT FROM AUTHOR]

In this paper, an alignment-free bioinformatics technique, termed the 20D-Dynamic Representation of Protein Sequences, is utilized to investigate the similarity/dissimilarity between Baculovirus and Echinococcus multilocularis genome sequences. In this method, amino acid sequences are depicted as 20D-dynamic graphs, comprising sets of "material points" in a 20-dimensional space. The spatial distribution of these material points is indicative of the sequence characteristics and is quantitatively described by sequence descriptors akin to those employed in dynamics, such as coordinates of the center of mass of the 20D-dynamic graph and the tensor of the moment of inertia of the graph (defined as a symmetric matrix). Each descriptor unveils distinct features of similarity and is employed to establish similarity relations among the examined sequences, manifested either as a symmetric distance matrix ("similarity matrix"), a classification map, or a phylogenetic tree. The classification maps are introduced as a new way of visualizing the similarity relations obtained using the 20D-Dynamic Representation of Protein Sequences. Some classification maps are obtained using the Principal Component Analysis (PCA) for the center of mass coordinates and normalized moments of inertia of 20D-dynamic graphs as input data. Although the method operates in a multidimensional space, we also apply some visualization techniques, including the projection of 20D-dynamic graphs onto a 2D plane. Studies on model sequences indicate that the method is of high quality, both graphically and numerically. Despite the high similarity observed among the sequences of E. multilocularis, subtle discrepancies can be discerned on the 2D graphs. Employing this approach has led to the discovery of numerous new similarity relations compared to our prior study conducted at the DNA level, using the 4D-Dynamic Representation of DNA/RNA Sequences, another alignment-free bioinformatics method also introduced by us. [ABSTRACT FROM AUTHOR]

This paper presents the first in-depth research on the biological and genomic properties of lytic rhizobiophage AP-J-162 isolated from the soils of the mountainous region of Dagestan (North Caucasus), which belongs to the centers of origin of cultivated plants, according to Vavilov N.I. The rhizobiophage host strains are nitrogen-fixing bacteria of the genus Sinorhizobium spp., symbionts of leguminous forage grasses. The phage particles have a myovirus virion structure. The genome of rhizobiophage AP-J-162 is double-stranded DNA of 471.5 kb in length; 711 ORFs are annotated and 41 types of tRNAs are detected. The closest phylogenetic relative of phage AP-J-162 is Agrobacterium phage Atu-ph07, but no rhizobiophages are known. The replicative machinery, capsid, and baseplate proteins of phage AP-J-162 are structurally similar to those of Escherichia phage T4, but there is no similarity between their tail protein subunits. Amino acid sequence analysis shows that 339 of the ORFs encode hypothetical or functionally relevant products, while the remaining 304 ORFs are unique. Additionally, 153 ORFs are similar to those of Atu_ph07, with one-third of the ORFs encoding different enzymes. The biological properties and genomic characteristics of phage AP-J-162 distinguish it as a unique model for exploring phage–microbe interactions with nitrogen-fixing symbiotic microorganisms. [ABSTRACT FROM AUTHOR]

Bicyclic peptides have attracted the interest of pharmaceutical companies because of their remarkable properties, putting them on a new path in medicine. Their conformational rigidity improves proteolytic stability and leads to rapid penetration into tissues via any possible route of administration. Moreover, elimination of renal metabolism is of great importance, for example, for people with a history of liver diseases. In addition, each ring can function independently, making bicyclic peptides extremely versatile molecules for further optimization. In this paper, we compared the potentiometric and spectroscopic properties studied by UV–vis, MCD, and EPR of four synthetic analogues of the bi-cyclic peptide c(PKKHP-c(CFWKTC)-PKKH) (BCL). In particular, we correlated the structural and spectral properties of complexes with coordinating abilities toward Cu(II) ions of MCL1 (Ac-PKKHPc(CFWKTC)PKKH-NH2) that contains the unbinding cycle and N- and C-terminal linear parts with two histidine residues, one per part; two monocyclic ligands containing one histidine residue, both in the N-terminal position, i.e., MCL2 (Ac-PKKHPc(CFWKTC)PKKS-NH2) and in the C-terminal position, i.e., MCL3 (Ac-PKKSPc(CFWKTC)PKKH-NH2), respectively; and the linear structure LNL (Ac-PKKHPSFWKTSPKKH-NH2). Potentiometric results have shown that the bicyclic structure promotes the involvement of the side chain imidazole donors in Cu(II) binding. On the other hand, the results obtained for the mono-cyclic analogues lead to the conclusion that the coordination of the histidine moiety as an anchoring group is promoted by its location in the peptide sequence further from the nonbinding cycle, strongly influencing the involvement of the amide donors in Cu(II) coordination. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

CRNDE is considered an oncogene expressed as long non-coding RNA. Our previous paper is the only one reporting CRNDE as a micropeptide-coding gene. The amino acid sequence of this micropeptide (CRNDEP) has recently been confirmed by other researchers. This study aimed at providing a mass spectrometry (MS)-based validation of the CRNDEP sequence and an investigation of how the differential expression of CRNDE(P) influences the metabolism and chemoresistance of ovarian cancer (OvCa) cells. We also assessed cellular localization changes of CRNDEP, looked for its protein partners, and bioinformatically evaluated its RNA-binding capacities. Herein, we detected most of the CRNDEP sequence by MS. Moreover, our results corroborated the oncogenic role of CRNDE, portraying it as the gene impacting carcinogenesis at the stages of DNA transcription and replication, affecting the RNA metabolism, and stimulating the cell cycle progression and proliferation, with CRNDEP being detected in the centrosomes of dividing cells. We also showed that CRNDEP is located in nucleoli and revealed interactions of this micropeptide with p54, an RNA helicase. Additionally, we proved that high CRNDE(P) expression increases the resistance of OvCa cells to treatment with microtubule-targeted cytostatics. Furthermore, altered CRNDE(P) expression affected the activity of the microtubular cytoskeleton and the formation of focal adhesion plaques. Finally, according to our in silico analyses, CRNDEP is likely capable of RNA binding. All these results contribute to a better understanding of the CRNDE(P) role in OvCa biology, which may potentially improve the screening, diagnosis, and treatment of this disease. [ABSTRACT FROM AUTHOR]

Simple Summary: As COVID-19 shifts from pandemic to endemic, emerging variants may be more or less virulent. Predicting whether an emerging COVID-19 variant has of high risk of causing severe disease is needed to plan for potential burdens on hospital capacity and protecting vulnerable populations. However, it takes time to do laboratory and animal experiments to determine whether a new genetic variant might be more severe, and the results may not be representative of when the virus infects humans. By the time there is epidemiological data on the severity of disease associated with a new variant, it can be too late for designing an optimal public health response. There is a critical need for computer models that can predict severe disease risk from genetic sequence data, which can be obtained from just the first few infections in a potential incoming wave. Two key challenges make computer modeling difficult: (1) sequence changes are complex, and (2) using historical data to predict future disease requires accounting for the confounding effects of changing patient demographics, improving therapeutics, and increased vaccination. In this paper, we introduce a novel interpretable deep learning architecture to solve this problem, demonstrating that it can make robust predictions for emerging variants. Through the COVID-19 pandemic, SARS-CoV-2 has gained and lost multiple mutations in novel or unexpected combinations. Predicting how complex mutations affect COVID-19 disease severity is critical in planning public health responses as the virus continues to evolve. This paper presents a novel computational framework to complement conventional lineage classification and applies it to predict the severe disease potential of viral genetic variation. The transformer-based neural network model architecture has additional layers that provide sample embeddings and sequence-wide attention for interpretation and visualization. First, training a model to predict SARS-CoV-2 taxonomy validates the architecture's interpretability. Second, an interpretable predictive model of disease severity is trained on spike protein sequence and patient metadata from GISAID. Confounding effects of changing patient demographics, increasing vaccination rates, and improving treatment over time are addressed by including demographics and case date as independent input to the neural network model. The resulting model can be interpreted to identify potentially significant virus mutations and proves to be a robust predctive tool. Although trained on sequence data obtained entirely before the availability of empirical data for Omicron, the model can predict the Omicron's reduced risk of severe disease, in accord with epidemiological and experimental data. [ABSTRACT FROM AUTHOR]

Simple Summary: Accurate identification of potential targets for drugs to interact with can accelerate drug development. The identification of drug–target interactions can provide insights into hidden drug efficacy. This paper presents a prediction model based on feature similarity fusion that can identify crucial features of drugs and targets to help predict drug–target interactions. The key to drug discovery is the identification of a target and a corresponding drug compound. Effective identification of drug–target interactions facilitates the development of drug discovery. In this paper, drug similarity and target similarity are considered, and graphical representations are used to extract internal structural information and intermolecular interaction information about drugs and targets. First, drug similarity and target similarity are fused using the similarity network fusion (SNF) method. Then, the graph isomorphic network (GIN) is used to extract the features with information about the internal structure of drug molecules. For target proteins, feature extraction is carried out using TextCNN to efficiently capture the features of target protein sequences. Three different divisions (CVD, CVP, CVT) are used on the standard dataset, and experiments are carried out separately to validate the performance of the model for drug–target interaction prediction. The experimental results show that our method achieves better results on AUC and AUPR. The docking results also show the superiority of the proposed model in predicting drug–target interactions. [ABSTRACT FROM AUTHOR]

Cytokinins (CKs) are among the hormones that regulate plants' growth and development, and the CKX and IPT genes, which are CK degradation and biosynthesis genes, respectively, play important roles in fine-tuning plants' cytokinin levels. However, the current research on the function of IPT and CKX in cucumber's growth, development, and response to abiotic stress is not specific enough, and their regulatory mechanisms are still unclear. In this study, we focused on the IPT and CKX genes in cucumber, analyzed the physiological and biochemical properties of their encoded proteins, and explored their expression patterns in different tissue parts and under low light, salt stress, and drought stress. Eight CsCKX and eight CsIPT genes were identified from the cucumber genome. We constructed a phylogenetic tree from the amino acid sequences and performed prediction analyses of the cis-acting elements of the CsCKX and CsIPT promoters to determine whether CsCKXs and CsIPTs are responsive to light, abiotic stress, and different hormones. We also performed expression analysis of these genes in different tissues, and we found that CsCKXs and CsIPTs were highly expressed in roots and male flowers. Thus, they are involved in the whole growth and development process of the plant. This paper provides a reference for further research on the biological functions of CsIPT and CsCKX in regulating the growth and development of cucumber and its response to abiotic stress. [ABSTRACT FROM AUTHOR]

Anticancer peptides (ACPs) are short protein sequences; they perform functions like some hormones and enzymes inside the body. The role of any protein or peptide is related to its structure and the sequence of amino acids that make up it. There are 20 types of amino acids in humans, and each of them has a particular characteristic according to its chemical structure. Current machine and deep learning models have been used to classify ACPs problems. However, these models have neglected Amino Acid Repeats (AARs) that play an essential role in the function and structure of peptides. Therefore, in this paper, ACPs offer a promising route for novel anticancer peptides by extracting AARs based on N-Grams and k-mers using two peptides' datasets. These datasets pointed to breast and lung cancer cells assembled and curated manually from the Cancer Peptide and Protein Database (CancerPPD). Every dataset consists of a sequence of peptides and their synthesis and anticancer activity on breast and lung cancer cell lines. Five different feature selection methods were used in this paper to improve classification performance and reduce the experimental costs. After that, ACPs were classified using four classifiers, namely AdaBoost, Random Forest Tree (RFT), Multi-class Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP). These classifiers were evaluated by applying five well-known evaluation metrics. Experimental results showed that the breast and lung ACPs classification process provided an accurate performance that reached 89.25% and 92.56%, respectively. In terms of AUC, it reached 95.35% and 96.92% for both breast and lung ACPs, respectively. The proposed classifiers performed competently somewhat equally in AUC, accuracy, precision, F-measures, and recall, except for Multi-class SVM-based feature selection, which showed superior performance. As a result, this paper significantly improved the predictive performance that can effectively distinguish ACPs as virtual inactive, experimental inactive, moderately active, and very active. [ABSTRACT FROM AUTHOR]

Interpreting biological phenomena at the molecular and cellular levels reveals the ways in which information that is specific to living organisms is processed: from the genetic record contained in a strand of DNA, to the translation process, and then to the construction of proteins that carry the flow and processing of information as well as reveal evolutionary mechanisms. The processing of a surprisingly small amount of information, i.e., in the range of 1 GB, contains the record of human DNA that is used in the construction of the highly complex system that is the human body. This shows that what is important is not the quantity of information but rather its skillful use—in other words, this facilitates proper processing. This paper describes the quantitative relations that characterize information during the successive steps of the "biological dogma", illustrating a transition from the recording of information in a DNA strand to the production of proteins exhibiting a defined specificity. It is this that is encoded in the form of information and that determines the unique activity, i.e., the measure of a protein's "intelligence". In a situation of information deficit at the transformation stage of a primary protein structure to a tertiary or quaternary structure, a particular role is served by the environment as a supplier of complementary information, thus leading to the achievement of a structure that guarantees the fulfillment of a specified function. Its quantitative evaluation is possible via using a "fuzzy oil drop" (FOD), particularly with respect to its modified version. This can be achieved when taking into account the participation of an environment other than water in the construction of a specific 3D structure (FOD-M). The next step of information processing on the higher organizational level is the construction of the proteome, where the interrelationship between different functional tasks and organism requirements can be generally characterized by homeostasis. An open system that maintains the stability of all components can be achieved exclusively in a condition of automatic control that is realized by negative feedback loops. This suggests a hypothesis of proteome construction that is based on the system of negative feedback loops. The purpose of this paper is the analysis of information flow in organisms with a particular emphasis on the role of proteins in this process. This paper also presents a model introducing the component of changed conditions and its influence on the protein folding process—since the specificity of proteins is coded in their structure. [ABSTRACT FROM AUTHOR]

Protein structure prediction represents a significant challenge in the field of bioinformatics, with the prediction of protein structures using backbone dihedral angles recently achieving significant progress due to the rise of deep neural network research. However, there is a trend in protein structure prediction research to employ increasingly complex neural networks and contributions from multiple models. This study, on the other hand, explores how a single model transparently behaves using sequence data only and what can be expected from the predicted angles. To this end, the current paper presents data acquisition, deep learning model definition, and training toward the final protein backbone angle prediction. The method applies a simple fully connected neural network (FCNN) model that takes only the primary structure of the protein with a sliding window of size 21 as input to predict protein backbone ϕ and ψ dihedral angles. Despite its simplicity, the model shows surprising accuracy for the ϕ angle prediction and somewhat lower accuracy for the ψ angle prediction. Moreover, this study demonstrates that protein secondary structure prediction is also possible with simple neural networks that take in only the protein amino-acid residue sequence, but more complex models are required for higher accuracies. [ABSTRACT FROM AUTHOR]

Simple Summary: It is widely accepted that climate change is a rapidly increasing threat to the aquaculture industry, with the biggest issue being a slow but continuous rise in water temperature and an associated decrease in dissolved oxygen in some areas. In New Zealand fish farms, during the hottest period of the year (January/February), increased water temperatures have been shown to be detrimental to Chinook salmon performance and are associated with mortalities. We report on the responses of Chinook salmon to a chronic, long-term increase in temperature and decrease in oxygen over a three-month period. We also identified, characterised, and developed relevant biomarkers for Chinook salmon and applied them to monitor responses to changes in the fish's environment. This paper provides evidence on how the use of molecular tools, alongside general health and haematological parameters, have the potential to better inform the aquaculture industry of stock health status, thereby enhancing resilience, efficiency, and productivity. In New Zealand, during the hottest periods of the year, some salmon farms in the Marlborough Sounds reach water temperatures above the optimal range for Chinook salmon. High levels of mortality are recorded during these periods, emphasising the importance of understanding thermal stress in this species. In this study, the responses of Chinook salmon (Oncorhynchus tshawytscha) to chronic, long-term changes in temperature and dissolved oxygen were investigated. This is a unique investigation due to the duration of the stress events the fish were exposed to. Health and haematological parameters were analysed alongside gene expression results to determine the effects of thermal stress on Chinook salmon. Six copies of heat shock protein 90 (HSP90) were discovered and characterised: HSP90AA1.1a, HSP90AA1.2a, HSP90AA1.1b, HSP90AA1.2b, HSP90AB1a and HSP90AB1b, as well as two copies of SOD1, named SOD1a and SOD1b. The amino acid sequences contained features similar to those found in other vertebrate HSP90 and SOD1 sequences, and the phylogenetic tree and synteny analysis provided conclusive evidence of their relationship to other vertebrate HSP90 and SOD1 genes. Primers were designed for qPCR to enable the expression of all copies of HSP90 and SOD1 to be analysed. The expression studies showed that HSP90 and SOD1 were downregulated in the liver and spleen in response to longer term exposure to high temperatures and lower dissolved oxygen. HSP90 was also downregulated in the gill; however, the results for SOD1 expression in the gill were not conclusive. This study provides important insights into the physiological and genetic responses of Chinook salmon to temperature and oxygen stress, which are critical for developing sustainable fish aquaculture in an era of changing global climates. [ABSTRACT FROM AUTHOR]

Integrons are genetic elements that store, express and exchange gene cassettes. These elements are characterized by containing a gene that codes for an integrase (intI), a cassette integration site (attI) and a variable region holding the cassettes. Using bioinformatics and molecular biology methods, a functional integron found in Aeromonas sp. 3925, a strain isolated from diarrheal stools, is described. To confirm the integron class, a phylogenetic analysis with amino acid sequences was conducted. The integrase was associated to class 4 integrases; however, it is clearly different from them. Thus, we classified the associated element as a class 4-like integron. We found that the integrase activity is not under the control of the SOS or catabolic repression, since the expression was not increased in the presence of mitomycin or arabinose. The class-4-like integron is located on the chromosome and contains two well-defined gene cassettes: aadA1 that confers resistance to streptomycin and lpt coding for a lipoprotein. It also includes eight Open Reading frames (ORFs) with unknown functions. The strain was characterized through a Multilocus Phylogenetic Analyses (MLPA) of the gyrB, gyrA, rpoD, recA, dnaJ and dnaX genes. The phylogenetic results grouped it into a different clade from the species already reported, making it impossible to assign a species. We resorted to undertaking complete genome sequencing and a phylogenomic analysis. Aeromonas sp. 3925 is related to A. media and A. rivipollensis clusters, but it is clearly different from these species. In silico DNA-DNA hybridization (isDDH) and Average Nucleotide Identity (ANI) analyses suggested that this isolate belongs to the genomospecies paramedia. This paper describes the first class 4-like integron in Aeromonas and contributes to the establishment of genomospecies paramedia. [ABSTRACT FROM AUTHOR]

Antibiotic resistance is a serious global health problem that poses a threat to the successful treatment of various bacterial infections, especially those caused by methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-resistant Enterococcus (VRE). Conventional treatment of MRSA and VRE infections is challenging and often requires alternative or combination therapies that may have limited efficacy, higher costs, and/or more adverse effects. Therefore, there is an urgent need to find new strategies to combat antibiotic-resistant bacteria. Probiotics and antimicrobial peptides (AMPs) are two promising approaches that have shown potential benefits in various diseases. Probiotics are live microorganisms that confer health benefits to the host when administered in adequate amounts. AMPs, usually produced with probiotic bacteria, are short amino acid sequences that have broad-spectrum activity against bacteria, fungi, viruses, and parasites. Both probiotics and AMPs can modulate the host immune system, inhibit the growth and adhesion of pathogens, disrupt biofilms, and enhance intestinal barrier function. In this paper, we review the current knowledge on the role of probiotics and AMPs in targeting multi-drug-resistant bacteria, with a focus on MRSA and VRE. In addition, we discuss future directions for the clinical use of probiotics. [ABSTRACT FROM AUTHOR]

Featured Application: This article belongs to the Section Computing and Artificial Intelligence. Ineffective protein feature representation poses problems in protein classification in hierarchical structures. Discrete wavelet transform (DWT) is a feature representation method which generates global and local features based on different wavelet families and decomposition levels. To represent protein sequences, the proper wavelet family and decomposition level must be selected. This paper proposed a hybrid optimization method using particle swarm optimization and the firefly algorithm (FAPSO) to choose the suitable wavelet family and decomposition level of wavelet transformation for protein feature representation. The suggested approach improved on the work of earlier researchers who, in most cases, manually selected the wavelet family and level of decomposition based solely on experience and not on data. The paper also applied the virtual class methods to overcome the error propagation problems in hierarchical classification. The effectiveness of the proposed method was tested on a G-Protein Coupled Receptor (GPCR) protein data set consisting of 5 classes at the family level, 38 classes at the subfamily level, and 87 classes at the sub-subfamily level. Based on the result obtained, the most selected wavelet family and decomposition level chosen to represent GPCR classes by FAPSO are Biorthogonal wavelets and decomposition level 1, respectively. The experimental results show that the representation of GPCR protein using the FAPSO algorithm with virtual classes can yield 97.9%, 86.9%, and 81.3% classification accuracy at the family, subfamily, and sub-subfamily levels, respectively. In conclusion, the result shows that the selection of optimized wavelet family and decomposition level by the FAPSO algorithm, and the virtual class method can be potentially used as the feature representation method and a hierarchical classification method for GPCR protein. [ABSTRACT FROM AUTHOR]

Dapsone (DDS), Rifampicin (RIF) and Ofloxacin (OFL) are drugs recommended by the World Health Organization (WHO) for the treatment of leprosy. In the context of leprosy, resistance to these drugs occurs mainly due to mutations in the target genes (Folp1, RpoB and GyrA). It is important to monitor antimicrobial resistance in patients with leprosy. Therefore, we performed a meta-analysis of drug resistance in Mycobacterium leprae and the mutational profile of the target genes. In this paper, we limited the study period to May 2022 and searched PubMed, Web of Science (WOS), Scopus, and Embase databases for identified studies. Two independent reviewers extracted the study data. Mutation and drug-resistance rates were estimated in Stata 16.0. The results demonstrated that the drug-resistance rate was 10.18% (95% CI: 7.85–12.51). Subgroup analysis showed the highest resistance rate was in the Western Pacific region (17.05%, 95% CI:1.80 to 13.78), and it was higher after 2009 than before [(11.39%, 7.46–15.33) vs. 6.59% (3.66–9.53)]. We can conclude that the rate among new cases (7.25%, 95% CI: 4.65–9.84) was lower than the relapsed (14.26%, 95 CI%: 9.82–18.71). Mutation rates of Folp1, RpoB and GyrA were 4.40% (95% CI: 3.02–5.77), 3.66% (95% CI: 2.41–4.90) and 1.28% (95% CI: 0.87–1.71) respectively, while the rate for polygenes mutation was 1.73% (0.83–2.63). For further analysis, we used 368 drug-resistant strains as research subjects and found that codons (Ser, Pro, Ala) on RpoB, Folp1 and GyrA are the most common mutation sites in the determining region (DRDR). In addition, the most common substitution patterns of Folp1, RpoB, and GyrA are Pro→Leu, Ser→Leu, and Ala→Val. This study found that a higher proportion of patients has developed resistance to these drugs, and the rate has increased since 2009, which continue to pose a challenge to clinicians. In addition, the amino acid alterations in the sequence of the DRDR regions and the substitution patterns mentioned in the study also provide new ideas for clinical treatment options. [ABSTRACT FROM AUTHOR]

Owing to the rapid changes in the antigenicity of influenza viruses, it is difficult for humans to obtain lasting immunity through antiviral therapy. Hence, tracking the dynamic changes in the antigenicity of influenza viruses can provide a basis for vaccines and drug treatments to cope with the spread of influenza viruses. In this paper, we developed a novel quantitative prediction method to predict the antigenic distance between virus strains using attribute network embedding techniques. An antigenic network is built to model and combine the genetic and antigenic characteristics of the influenza A virus H3N2, using the continuous distributed representation of the virus strain protein sequence (ProtVec) as a node attribute and the antigenic distance between virus strains as an edge weight. The results show a strong positive correlation between supplementing genetic features and antigenic distance prediction accuracy. Further analysis indicates that our prediction model can comprehensively and accurately track the differences in antigenic distances between vaccines and influenza virus strains, and it outperforms existing methods in predicting antigenic distances between strains. [ABSTRACT FROM AUTHOR]

Spider venoms are composed, among other substances, of peptide toxins whose selectivity for certain physiological targets has made them powerful tools for applications such as bioinsecticides, analgesics, antiarrhythmics, antibacterials, antifungals and antimalarials, among others. Bioinsecticides are an environmentally friendly alternative to conventional agrochemicals. In this paper, the primary structure of an insecticidal peptide was obtained from the venom gland transcriptome of the ctenid spider Phoneutria depilata (Transcript ID PhdNtxNav24). The peptide contains 53 amino acids, including 10 Cys residues that form 5 disulfide bonds. Using the amino acid sequence of such peptide, a synthetic gene was constructed de novo by overlapping PCRs and cloned into an expression vector. A recombinant peptide, named delta-ctenitoxin (rCtx-4), was obtained. It was expressed, folded, purified and validated using mass spectrometry (7994.61 Da). The insecticidal activity of rCtx-4 was demonstrated through intrathoracic injection in crickets (LD50 1.2 μg/g insect) and it was not toxic to mice. rCtx-4 is a potential bioinsecticide that could have a broad spectrum of applications in agriculture. [ABSTRACT FROM AUTHOR]

The paper contains a discussion on mathematical modifying and redesigning DNA with the use of Markov chains. We give a simple mathematical technique for overwriting missing parts of DNA. With a certain probability (without even knowing the function of the missing codon) we can find a synonymous codon, so that there is no frequency change in amino acid sequences of proteins. We use Markov Chain to analyze the dependencies in DNA sequence of the human gene Alpha 1,3-Galactosyltransfe rase 2. We include a theoretical introduction which facilitates the understanding of the paper for non-mathematicians, especially for biologists not familiar with the theory of Markov chains. [ABSTRACT FROM AUTHOR]

In this paper, the influence of external terahertz electromagnetic fields with different frequencies of 4 THz, 10 THz, 15 THz, and 20 THz on the permeability of the Kv1.2 voltage-gated potassium ion channel on the nerve cell membrane was studied using the combined model of the "Constant Electric Field-Ion Imbalance" method by molecular dynamics. We found that although the applied terahertz electric field does not produce strong resonance with the –C=O groups of the conservative sequence T-V-G-Y-G amino acid residue of the selective filter (SF) of the channel, it would affect the stability of the electrostatic bond between potassium ions and the carbonyl group of T-V-G-Y-G of SF, and it would affect the stability of the hydrogen bond between water molecules and oxygen atoms of the hydroxyl group of the 374THR side chain at the SF entrance, changing the potential and occupied states of ions in the SF and the occurrence probability of the permeation mode of ions and resulting in the change in the permeability of the channel. Compared with no external electric field, when the external electric field with 15 THz frequency is applied, the lifetime of the hydrogen bond is reduced by 29%, the probability of the "soft knock on" mode is decreased by 46.9%, and the ion flux of the channel is activated by 67.7%. Our research results support the view that compared to "direct knock-on", "soft knock-on" is a slower permeation mode. [ABSTRACT FROM AUTHOR]

The biomedical field has always fostered innovation and the development of various new technologies. Beginning in the last century, demand for picoampere-level current detection in biomedicine has increased, leading to continuous breakthroughs in biosensor technology. Among emerging biomedical sensing technologies, nanopore sensing has shown great potential. This paper reviews nanopore sensing applications, such as chiral molecules, DNA sequencing, and protein sequencing. However, the ionic current for different molecules differs significantly, and the detection bandwidths vary as well. Therefore, this article focuses on current sensing circuits, and introduces the latest design schemes and circuit structures of different feedback components of transimpedance amplifiers mainly used in nanopore DNA sequencing. [ABSTRACT FROM AUTHOR]

Simple Summary: This article is mainly about that molecular characterization and expression pattern of leptin in yellow cheek carp (Elopichthys bambusa) and its transcriptional changes in response to fasting and refeeding. In this study, the authors used PCR to clone the CDS of leptin in yellow cheek carp, and analyzed the sequence differences of the gene with other species, constructed the phylogenetic tree, used real-time PCR for analyzing the expression of leptin in different tissues, including the expression of leptin in the brain and liver after fasting–refeeding of yellow cheek carp. This paper found that the full-length cDNA sequence of Eblep was 1140 bp and the length of the open reading frame (ORF), which can encode a protein of 174 amino acids, was 525 bp. The Eblep mRNA transcript was detected in all tested tissues, with the highest expression in the liver and lowest expression in the spleen. It was found that the change in the mRNA expression of EbLep may be an adaptive strategy for different energy levels by studying the expression of EbLep mRNA in the brain and liver under fasting and refeeding. Leptin, a secretory protein encoded by obese genes, plays an important role in regulating feeding and energy metabolism in fish. To study the structure and function of the Leptin gene in yellow cheek carp (Elopichthys bambusa), the full-length cDNA sequence of leptin was cloned, named EbLep. The full-length cDNA of Eblep was 1140 bp, and the length of the open reading frame (ORF), which can encode a protein of 174 amino acids, was 525 bp. The signal peptide was predicted to contain 33 amino acids. Sequence alignment showed that the amino acid sequence of Leptin was conserved in cyprinid fish. Despite large differences between primary structures, the tertiary structure of the EbLep protein was similar to that of the human protein and had four α-helices. The EbLep mRNA transcript was detected in all tested tissues, with the highest expression in the liver and lowest expression in the spleen. In this study, short-term fasting significantly increased the mRNA expression of EbLep in the liver, which returned to a normal level after 6 days of refeeding and was significantly lower than the normal level after 28 days of refeeding. In the brain, the mRNA expression of EbLep significantly decreased during short-term fasting and significantly increased to a higher value than the control group after 1 h of refeeding. It then rapidly decreased to a lower value than the control group after 6 h of refeeding, returning to the normal level after 1 day of refeeding, and significantly decreasing to a lower value than the control group after 28 days of refeeding. To sum up, the change in the mRNA expression of EbLep in the brain and liver may be an adaptive strategy for different energy levels. [ABSTRACT FROM AUTHOR]

Featured Application: Protein and peptide identification based on tandem mass spectrometry is a pillar technology in proteomics research. In recent years, increasing numbers of researchers have utilized deep learning to tackle challenges in proteomics. For example, catalyzed by deep learning, AlphaFold has achieved unparalleled levels of accuracy in protein-structure prediction. Prior to studying the structure and function of proteins in cells or tissues, it is essential to determine the sequences of amino acids in peptides or proteins. De novo peptide sequencing can be used to directly infer the peptide sequence from a tandem mass spectrum without the requirement for a reference sequence database, making it particularly suitable for the determination of protein sequences of unknown species, monoclonal antibodies, and cancer neoantigens. The de novo peptide-sequencing method can be used to directly infer the peptide sequence from a tandem mass spectrum. It has the advantage of not relying on protein databases and plays a key role in the determination of the protein sequences of unknown species, monoclonal antibodies, and cancer neoantigens. In this paper, we propose a method based on graph convolutional neural networks and convolutional neural networks, Denovo-GCN, for de novo peptide sequencing. We constructed an undirected graph based on the mass difference between the spectral peaks in a tandem mass spectrum. The features of the nodes on the spectrum graph, which represent the spectral peaks, were the matching information of the peptide sequence and the mass spectrum. Next, the Denovo-GCN used CNN to extract the features of the nodes. The correlation between the nodes was represented by an adjacency matrix, which aggregated the features of neighboring nodes. Denovo-GCN provides a complete end-to-end training and prediction framework to sequence patterns of peptides. Our experiments on various data sets from different species show that Denovo-GCN outperforms DeepNovo with a relative improvement of 13.7–25.5% in terms of the peptide-level recall. [ABSTRACT FROM AUTHOR]

The formation of mature mRNA requires cutting introns and splicing exons. The occurrence of splicing involves the participation of the spliceosome. Common spliceosomes mainly include five snRNPs: U1, U2, U4/U6, and U5. SF3a2, an essential component of spliceosome U2 snRNP, participates in splicing a series of genes. There is no definition of SF3a2 in plants. The paper elaborated on SF3a2s from a series of plants through protein sequence similarity. We constructed the evolutionary relationship of SF3a2s in plants. Moreover, we analyzed the similarities and differences in gene structure, protein structure, the cis-element of the promoter, and expression pattern; we predicted their interacting proteins and constructed their collinearity. We have preliminarily analyzed SF3a2s in plants and clarified the evolutionary relationship between different species; these studies can better serve for in-depth research on the members of the spliceosome in plants. [ABSTRACT FROM AUTHOR]

Trichoderma reesei is widely applied as the major industrial fungus for the production of cellulases used for the conversion of lignocellulosic biomass to biofuels and other biobased products. The protein secretion pathway is vital for cellulase secretion, but few reports are related to the role of the vacuole in cellulase production. Here, we identified a novel vacuolar serine protease gene spt1 and investigated the ability of T. reesei to secrete cellulases by disrupting, complementing and overexpressing the spt1 gene. Amino acid sequence analysis of the Spt1 protein showed that it belongs to the subtilisin S8 family and has the conserved catalytic triples (Asp, His, Ser) of the serine protease. The deletion of spt1 did not lead to a decrease in extracellular protease activity, and the observation of mycelia with the Spt1–eGFP fusion expression and the vacuolar membrane dye FM4-64 staining confirmed that Spt1 was an intracellular protease located in the vacuoles of T. reesei. However, the spt1 gene deletion significantly reduced spore production and cellulase secretion, while the spt1 complementation recovered these traits to those of the parental strain. When spt1 was overexpressed by using its native promoter and introducing multiple copies, the cellulase secretion was improved. Furthermore, a strong promoter, Pcdna1, was used to drive the spt1 overexpression, and it was found that the cellulase production was significantly enhanced. Specifically, the filter paper activity of the spt1 overexpression strain SOD-2 reached 1.36 U/mL, which was 1.72 times higher than that of the parental strain. These findings demonstrated that the spt1 gene can be a powerful target for increasing cellulase production in T. reesei, which suggests a possible important role of the vacuole in the cellulase secretion pathway and provides new clues for improving strains for efficient cellulase production. [ABSTRACT FROM AUTHOR]

Almost 65% of the human protein supply in the world originates from plants, with legumes being one of the highest contributors, comprising between 20 and 40% of the protein supply. Bioactive peptides from various food sources including legumes have been reported to show efficacy in modulating starch digestion and glucose absorption. This paper will provide a comprehensive review on recent in vitro studies that have been performed on leguminous antidiabetic peptides, focusing on the α-amylase inhibitor, α-glucosidase inhibitor, and dipeptidyl peptidase-IV (DPP-IV) inhibitor. Variations in legume cultivars and methods affect the release of peptides. Different methods have been used, such as in sample preparation, including fermentation (t, T), germination (t), and pre-cooking; in protein extraction, alkaline extraction, isoelectric precipitation, phosphate buffer extraction, and water extraction; in protein hydrolysis enzyme types and combination, enzyme substrate ratio, pH, and time; and in enzyme inhibitory assays, positive control type and concentration, inhibitor or peptide concentration, and the unit of inhibitory activity. The categorization of the relative scale of inhibitory activities among legume samples becomes difficult because of these method differences. Peptide sequences in samples were identified by means of HPLC/MS. Software and online tools were used in bioactivity prediction and computational modelling. The identification of the types and locations of chemical interactions between the inhibitor peptides and enzymes and the type of enzyme inhibition were achieved through computational modelling and enzyme kinetic studies. [ABSTRACT FROM AUTHOR]

Amyloid aggregates play a major role in diseases as well as in normal physiological function. Receptor-interacting protein kinases 1 and 3 (RIP1/RIP3) aggregates complexes in cellular necroptosis is one example of protein aggregation in normal cellular function. Although recently there have been several studies on full kinase proteins aggregation, the aggregation potential of small peptide sequences of RIP1/RIP3, the physicochemical properties, and the potential in biomedical applications have not been explored. Hence, in this paper, we study the aggregation propensity of peptides consisting of four and twelve amino acid sequences in the RHIM region of RIP1/RIP3 proteins that are known to drive the beta-sheet formation and the subsequent aggregation. The aggregation kinetics, physicochemical characterization, mechanosensitive properties, cellular effects, and potential as a cancer drug depot have been investigated. The results show that the number and concentration of amino acids play a role in amyloid-like aggregates' properties. Further, the aggregates when formulated with cisplatin-induced significant lung cancer cell toxicity compared to an equal amount of cisplatin with and without ultrasound. The study would serve as a platform for further investigation on RIP1/RIP3 peptide and protein aggregates, their role in multiple cellular functions and diseases, and their potential as drug depots. [ABSTRACT FROM AUTHOR]

Research on food-derived bioactive peptides is expanding and the need for a convenient online platform that combines a foolproof and intuitive user interface with a reliable database and tools for prediction and analysis is rising. In this regard, this paper presents an open-access web-based platform PepLab (Peptides Laboratory). The database contains 2764 peptide sequence entries, grouped into sixteen classes according to their biological activity and into seven classes according to the source from which they were derived. Moreover, it includes bioinformatic tools for their processing and analysis. The DMpep tool allows extracting information about a number of physicochemical characteristics of a peptide or a set of peptides, including those that are not recorded in the database. The main advantages of PepLab are a user-friendly interface, a responsive design, and optimized search engines for better visibility on the Internet. Prediction of non-reported activity is available based on amino acid sequence analysis. In addition, users can download data and results in a convenient format (text and/or graphic) that was limited in the existing platforms. In this way, the presented PepLab platform will be helpful for researchers from various fields—bioinformatics, pharmaceuticals, food sciences, dietetics, biotechnology, analytical chemistry, etc. [ABSTRACT FROM AUTHOR]

Simple Summary: Conotoxins are small, structured components found in the venom of predatory cone snails. They were proven to be valuable probes and models for drug discovery and protein evolution studies. Conotoxins present an opportunity to study protein divergence and discover potential human therapeutic landscapes. Although there is considerable literature on conotoxin evolution and activity, what pushed conotoxin divergence remains unclear. Hence, in this paper, we conducted a two-phase study that investigated conotoxin evolution in terms of divergence, followed by structural analysis to determine the relevant structural elements. By understanding the evolution of conotoxins, we identified patterns that account for their superior specificity. The results revealed similarities based on the cone snail's diet preference. The structural elements are in synch with their target prey preference as if cone snails evolved to fine-tune their conopeptide armory to respond to evolutionary pressures by producing conotoxins selective for their prey of choice. We identified several structural elements that account for this specificity. Conservation patterns are observed within diet subgroups but are divergent from other groups. Predatory cone snails (Conus) developed a sophisticated neuropharmacological mechanism to capture prey, escape against other predators, and deter competitors. Their venom's remarkable specificity for various ion channels and receptors is an evolutionary feat attributable to the venom's variety of peptide components (conotoxins). However, what caused conotoxin divergence remains unclear and may be related to the role of prey shift. Principal component analysis revealed clustering events within diet subgroups indicating peptide sequence similarity patterns based on the prey they subdue. Molecular analyses using multiple sequence alignment and structural element analysis were conducted to observe the events at the molecular level that caused the subgrouping. Three distinct subgroups were identified. Results showed homologous regions and conserved residues within diet subgroups but divergent between other groups. We specified that these structural elements caused subgrouping in alpha conotoxins that may play a role in function specificity. In each diet subgroup, amino acid character, length of intervening amino acids between cysteine residues, and polypeptide length influenced subgrouping. This study provides molecular insights into the role of prey shift, specifically diet preference, in conotoxin divergence. [ABSTRACT FROM AUTHOR]

This document is an editorial for a special issue of the journal Microorganisms titled "Advances in Acanthamoeba." Acanthamoeba is a type of free-living amoeba that can act as an opportunistic parasite, causing rare but dangerous diseases in humans and animals. The editorial highlights the need for further research to better understand the biology and pathogenicity of Acanthamoeba. It also discusses the challenges in classifying Acanthamoeba species based on morphological criteria and the importance of genetic data for accurate identification. The special issue includes articles covering various topics related to Acanthamoeba, such as cyst characteristics, genetic markers, and evolutionary history. [Extracted from the article]

For over 20 years, bovine beta-casein has been a subject of increasing scientific interest because its genetic A1 variant during gastrointestinal digestion releases opioid-like peptide β-casomorphin-7 (β-CM-7). Since β-CM-7 is involved in the dysregulation of many physiological processes, there is a growing discussion of whether the consumption of the β-casein A1 variant has an influence on human health. In the last decade, the number of papers dealing with this problem has substantially increased. The newest clinical studies on humans showed a negative effect of variant A1 on serum glutathione level, digestive well-being, cognitive performance score in children, and mood score in women. Scientific reports in this field can affect the policies of dairy cattle breeders and the milk industry, leading to the elimination of allele A1 in dairy cattle populations and promoting milk products based on milk from cows with the A2A2 genotype. More scientific proof, especially in well-designed clinical studies, is necessary to determine whether a little difference in the β-casein amino acid sequence negatively affects the health of milk consumers. [ABSTRACT FROM AUTHOR]

Simple Summary: Rho family of proteins are involved in cytoskeletal organization, cell mobility and polarity, and are implicated in cancer morphogenesis. The structure and function of the Rho homologs from higher-level organisms are well studied, but not from the lower-level organisms. Such as over 95% of the known structures of Rho GTPases are from higher-order mammalian organisms, with only three structures of Rho homologs reported to date from lower-level, single-celled organisms. In this paper we report the crystal structure of Rho1 from Schizosaccharomyces pombe, also called fission yeast (SpRho1), in complex with GDP in the presence of Mg2+ at 2.63-Å resolution, to broaden our understanding of Rho homologs in lower-level organisms. Although the overall structure is similar to that of known Rho homologs, we observed subtle differences at the Switch I and II regions, in β2 and β3, and in the Rho insert domain and loop from Phe107 to Pro112. Combined with literature and sequence analyses, we suggest that the Switch regions and Rho insert domain may contribute to downstream kinase activation in different species through their interactions with different effectors and regulators; and the conservation and divergence of Rho GTPases among difference species and provide evolutionary insight for SpRho1. While many studies have reported the evolutionary development of Rho GTPases based on their amino acid sequences, the present study, for the first time, explores these evolutionary aspects based on structure. Our analysis indicates that SpRho is evolutionarily closer to HsRhoC than HsRhoA, as previously believed. The Rho protein, a homolog of Ras, is a member of the Ras superfamily of small GTPases. Rho family proteins are involved in cytoskeletal organization, cell mobility, and polarity, and are implicated in cancer morphogenesis. Although Rho homologs from higher-order mammalian organisms are well studied, there are few studies examining Rho proteins in lower-level single-celled organisms. Here, we report on the crystal structure of Rho1 from Schizosaccharomyces pombe (SpRho1) in complex with GDP in the presence of Mg2+ at a 2.78 Å resolution. The overall structure is similar to that of known Rho homologs, including human RhoA, human RhoC, and Aspergillus fumigatus Rho1 (AfRho1), with some exceptions. We observed subtle differences at the Switch I and II regions, in β2 and β3, and in the Rho insert domain and loop from Phe107 to Pro112. Our analysis suggests that SpRho is evolutionarily closer to HsRhoC than HsRhoA, as previously believed. [ABSTRACT FROM AUTHOR]

The classification of protein sequences provides valuable insights into bioinformatics. Most existing methods are based on sequence alignment algorithms, which become time-consuming as the size of the database increases. Therefore, there is a need to develop an improved method for effectively classifying protein sequences. In this paper, we propose a novel accumulated natural vector method to cluster protein sequences at a lower time cost without reducing accuracy. Our method projects each protein sequence as a point in a 250-dimensional space according to its amino acid distribution. Thus, the biological distance between any two proteins can be easily measured by the Euclidean distance between the corresponding points in the 250-dimensional space. The convex hull analysis and classification perform robustly on virus and bacteria datasets, effectively verifying our method. [ABSTRACT FROM AUTHOR]

Proteins are the fundamental biological macromolecules which underline practically all biological activities. Protein–protein interactions (PPIs), as they are known, are how proteins interact with other proteins in their environment to perform biological functions. Understanding PPIs reveals how cells behave and operate, such as the antigen recognition and signal transduction in the immune system. In the past decades, many computational methods have been developed to predict PPIs automatically, requiring less time and resources than experimental techniques. In this paper, we present a comparative study of various graph neural networks for protein–protein interaction prediction. Five network models are analyzed and compared, including neural networks (NN), graph convolutional neural networks (GCN), graph attention networks (GAT), hyperbolic neural networks (HNN), and hyperbolic graph convolutions (HGCN). By utilizing the protein sequence information, all of these models can predict the interaction between proteins. Fourteen PPI datasets are extracted and utilized to compare the prediction performance of all these methods. The experimental results show that hyperbolic graph neural networks tend to have a better performance than the other methods on the protein-related datasets. [ABSTRACT FROM AUTHOR]

For mass spectrometry-based diagnostics of microorganisms, matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) is currently routinely used to identify urinary tract pathogens. However, it requires a lengthy culture step for accurate pathogen identification, and is limited by a relatively small number of available species in peptide spectral libraries (≤3329). Here, we propose a method for pathogen identification that overcomes the above limitations, and utilizes the MALDI-TOF/TOF MS instrument. Tandem mass spectra of the analyzed peptides were obtained by chemically activated fragmentation, which allowed mass spectrometry analysis in negative and positive ion modes. Peptide sequences were elucidated de novo, and aligned with the non-redundant National Center for Biotechnology Information Reference Sequence Database (NCBInr). For data analysis, we developed a custom program package that predicted peptide sequences from the negative and positive MS/MS spectra. The main advantage of this method over a conventional MALDI-TOF MS peptide analysis is identification in less than 24 h without a cultivation step. Compared to the limited identification with peptide spectra libraries, the NCBI database derived from genome sequencing currently contains 20,917 bacterial species, and is constantly expanding. This paper presents an accurate method that is used to identify pathogens grown on agar plates, and those isolated directly from urine samples, with high accuracy. [ABSTRACT FROM AUTHOR]

Understanding gene functions and their associated abnormal phenotypes is crucial in the prevention, diagnosis and treatment against diseases. The Human Phenotype Ontology (HPO) is a standardized vocabulary for describing the phenotype abnormalities associated with human diseases. However, the current HPO annotations are far from completion, and only a small fraction of human protein-coding genes has HPO annotations. Thus, it is necessary to predict protein-phenotype associations using computational methods. Protein sequences can indicate the structure and function of the proteins, and interacting proteins are more likely to have same function. It is promising to integrate these features for predicting HPO annotations of human protein. We developed GraphPheno, a semi-supervised method based on graph autoencoders, which does not require feature engineering to capture deep features from protein sequences, while also taking into account the topological properties in the protein–protein interaction network to predict the relationships between human genes/proteins and abnormal phenotypes. Cross validation and independent dataset tests show that GraphPheno has satisfactory prediction performance. The algorithm is further confirmed on automatic HPO annotation for no-knowledge proteins under the benchmark of the second Critical Assessment of Functional Annotation, 2013–2014 (CAFA2), where GraphPheno surpasses most existing methods. Further bioinformatics analysis shows that predicted certain phenotype-associated genes using GraphPheno share similar biological properties with known ones. In a case study on the phenotype of abnormality of mitochondrial respiratory chain, top prioritized genes are validated by recent papers. We believe that GraphPheno will help to reveal more associations between genes and phenotypes, and contribute to the discovery of drug targets. [ABSTRACT FROM AUTHOR]

Multiple Sequence Alignment (MSA) is one of the most fundamental methodologies in Bioinformatics and the method capable of arranging DNA or protein sequences to detect regions of similarity. Even on cutting-edge workstations, the MSA procedure requires a significant amount of time regarding its execution time. This paper demonstrates how to utilize Extensa Explorer by Tensilica (Cadence) to create an extended instruction set to meet the requirements of some of the most widely used algorithms in Bioinformatics for MSA analysis. Kalign showed the highest acceleration, reducing Instruction Fetches (IF) and Execution Time (ET) by 30.29 and 43.49 percent, respectively. Clustal had acceleration of 14.2% in IF and 17.9% in ET, whereas Blast had 12.35% in IF and 16.25% in ET. [ABSTRACT FROM AUTHOR]

Xylanases (EC 3.2.1.8) hydrolyze the hemicellulose of plant cell walls. Xylanases are used in the food and paper industries and for bioconversion of lignocellulose to biofuel. In this work, the producer-strain with four copies of the xAor xylanase gene was organized in two tandem copies for optimal expression in Komagataella phaffii T07 yeast. The secreted 35 kDa xylanase was purified from culture medium by gel filtration on Sephadex G-25 and anion exchange chromatography on DEAE-Sepharose 6HF. Tryptic peptides of the recombinant enzyme were analyzed by liquid chromatography-tandem mass spectrometry where the amino acid sequence corresponded to Protein Accession # O94163 for Endo-1,4-beta-xylanase from Aspergillus oryzae RIB40. The recombinant xylanase was produced in a bioreactor where the secreted enzyme hydrolyzed oat xylane with an activity of 258240 IU/mL. High activity in the culture medium suggested xylanase could be used for industrial applications without being purified or concentrated. The pH optimum for xylanase xAor was 7.5, though the enzyme was active from pH 2.5 to pH 10. Xylanase was active at temperatures from 35 °C to 85 °C with a maximum at 60 °C. In conclusion, this protocol yields soluble, secreted xylanase suitable for industrial scale production. [ABSTRACT FROM AUTHOR]

Epitopes are short amino acid sequences that define the antigen signature to which an antibody or T cell receptor binds. In light of the current pandemic, epitope analysis and prediction are paramount to improving serological testing and developing vaccines. In this paper, known epitope sequences from SARS-CoV, SARS-CoV-2, and other Coronaviridae were leveraged to identify additional antigen regions in 62K SARS-CoV-2 genomes. Additionally, we present epitope distribution across SARS-CoV-2 genomes, locate the most commonly found epitopes, and discuss where epitopes are located on proteins and how epitopes can be grouped into classes. The mutation density of different protein regions is presented using a big data approach. It was observed that there are 112 B cell and 279 T cell conserved epitopes between SARS-CoV-2 and SARS-CoV, with more diverse sequences found in Nucleoprotein and Spike glycoprotein. [ABSTRACT FROM AUTHOR]

Cerebrospinal fluid (CSF) exists in the surrounding spaces of mammalian central nervous systems (CNS); therefore, there are numerous potential protein biomarkers associated with CNS disease in CSF. Currently, approximately 4300 proteins have been identified in CSF by protein profiling. However, due to the diverse modifications, as well as the existing technical limits, large-scale protein identification in CSF is still considered a challenge. Inspired by computational methods, this paper proposes a deep learning framework, named DenSec, for secreted protein prediction in CSF. In the first phase of DenSec, all input proteins are encoded as a matrix with a fixed size of 1000 × 20 by calculating a position-specific score matrix (PSSM) of protein sequences. In the second phase, a dense convolutional network (DenseNet) is adopted to extract the feature from these PSSMs automatically. After that, Transformer with a fully connected dense layer acts as classifier to perform a binary classification in terms of secretion into CSF or not. According to the experiment results, DenSec achieves a mean accuracy of 86.00% in the test dataset and outperforms the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The novel BIOPEP-UWM Virtual database is designed as a repository of peptide sequences whose bioactivity or taste information was the result of in silico predictions. It is a tool complementary to the existing BIOPEP-UWM database summarizing the results of experimental data on bioactive peptides. The layout and organization of the new database are identical to those of the existing BIOPEP-UWM database of bioactive peptides. The peptide data record includes the following information: name; sequence and function information (understood as information about the predicted target biomacromolecule); bibliographic data with the reference paper describing the peptide; additional information, including the peptide structure, annotated using chemical codes as well as the specification of the method used for bioactivity prediction; information about other activities discovered experimentally or predicted using computational methods; peptide taste (if available); and a database reference tab providing information about compound annotations in other databases (if available). [ABSTRACT FROM AUTHOR]

Simple Summary: Due to most traditional high-throughput experiments are tedious and laborious in identifying potential protein–protein interaction. To better improve accuracy prediction in protein–protein interactions. We proposed a novel computational method that can identify unknown protein–protein interaction efficiently and hope this method can provide a helpful idea and tool for proteomics research. Protein–protein interactions (PPIs) play an essential role in many biological cellular functions. However, it is still tedious and time-consuming to identify protein–protein interactions through traditional experimental methods. For this reason, it is imperative and necessary to develop a computational method for predicting PPIs efficiently. This paper explores a novel computational method for detecting PPIs from protein sequence, the approach which mainly adopts the feature extraction method: Locality Preserving Projections (LPP) and classifier: Rotation Forest (RF). Specifically, we first employ the Position Specific Scoring Matrix (PSSM), which can remain evolutionary information of biological for representing protein sequence efficiently. Then, the LPP descriptor is applied to extract feature vectors from PSSM. The feature vectors are fed into the RF to obtain the final results. The proposed method is applied to two datasets: Yeast and H. pylori, and obtained an average accuracy of 92.81% and 92.56%, respectively. We also compare it with K nearest neighbors (KNN) and support vector machine (SVM) to better evaluate the performance of the proposed method. In summary, all experimental results indicate that the proposed approach is stable and robust for predicting PPIs and promising to be a useful tool for proteomics research. [ABSTRACT FROM AUTHOR]

Nematodes are non-segmented roundworms evenly distributed with various habitats ranging to approximately every ecological extremity. These are the least studied organisms despite being the most diversified group. Nematodes are the most critical equilibrium-maintaining factors, having implications on the yield and health of plants as well as well-being of animals. However, taxonomic knowledge about nematodes is scarce. As a result of the lack of precise taxonomic features, nematode taxonomy remains uncertain. Morphology-based identification has proved inefficacious in identifying and exploring the diversity of nematodes, as there are insufficient morphological variations. Different molecular and new evolving methodologies have been employed to augment morphology-based approaches and bypass these difficulties with varying effectiveness. These identification techniques vary from molecular-based targeting DNA or protein-based targeting amino acid sequences to methods for image processing. High-throughput approaches such as next-generation sequencing have also been added to this league. These alternative approaches have helped to classify nematodes and enhanced the base for increased diversity and phylogeny of nematodes, thus helping to formulate increasingly more nematode bases for use as model organisms to study different hot topics about human well-being. Here, we discuss all the methods of nematode identification as an essential shift from classical morphometric studies to the most important modern-day and molecular approaches for their identification. Classification varies from DNA/protein-based methods to the use of new emerging methods. However, the priority of the method relies on the quality, quantity, and availability of nematode resources and down-streaming applications. This paper reviews all currently offered methods for the detection of nematodes and known/unknown and cryptic or sibling species, emphasizing modern-day methods and budding molecular techniques. [ABSTRACT FROM AUTHOR]

Nanopores have attracted widespread attention in DNA sequencing and protein or biomarker detection, owning to the single-molecule-scale detection accuracy. Despite the most use of naturally biological nanopores before, solid-state nanopores are widely developed with strong robustness, controllable sizes and geometries, a wide range of materials available, as well as flexible manufacturing. Therefore, various techniques typically based on focused ion beam or electron beam have been explored to drill nanopores directly on free-standing nanofilms. To further reduce and sculpt the pore size and shape for nano or sub-nano space-time sensing precision, various controllable shrinking technologies have been employed. Correspondingly, high-energy-beam-induced contraction with direct visual feedback represents the most widely used. The ability to change the pore diameter was attributed to surface tension induced original material migration into the nanopore center or new material deposition on the nanopore surface. This paper reviews typical solid-state nanopore shrinkage technologies, based on the careful summary of their principles and characteristics in particularly size and morphology changes. Furthermore, the advantages and disadvantages of different methods have also been compared completely. Finally, this review concludes with an optimistic outlook on the future of solid-state nanopores. [ABSTRACT FROM AUTHOR]

Botulinum neurotoxins (BoNT) are a group of clostridial toxins that cause the potentially fatal neuroparalytic disease botulism. Although highly toxic, BoNTs are utilized as therapeutics to treat a range of neuromuscular conditions. Several serotypes (BoNT/A-/G, /X) have been identified with vastly differing toxicological profiles. Each serotype can be further sub-categorised into subtypes due to subtle variations in their protein sequence. These minor changes have been attributed to differences in both the duration of action and potency for BoNT/A subtypes. BoNTs are composed of three domains—a cell-binding domain, a translocation domain, and a catalytic domain. In this paper, we present the crystal structures of the botulinum neurotoxin A2 cell binding domain, both alone and in complex with its receptor ganglioside GD1a at 1.63 and 2.10 Å, respectively. The analysis of these structures reveals a potential redox-dependent Lys-O-Cys bridge close to the ganglioside binding site and a hinge motion between the HCN and HCC subdomains. Furthermore, we make a detailed comparison with the previously reported HC/A2:SV2C structure for a comprehensive structural analysis of HC/A2 receptor binding. [ABSTRACT FROM AUTHOR]

Drug discovery (DD) is a time-consuming and expensive process. Thus, the industry employs strategies such as drug repositioning and drug repurposing, which allows the application of already approved drugs to treat a different disease, as occurred in the first months of 2020, during the COVID-19 pandemic. The prediction of drug–target interactions is an essential part of the DD process because it can accelerate it and reduce the required costs. DTI prediction performed in silico have used approaches based on molecular docking simulations, including similarity-based and network- and graph-based ones. This paper presents MPS2IT-DTI, a DTI prediction model obtained from research conducted in the following steps: the definition of a new method for encoding molecule and protein sequences onto images; the definition of a deep-learning approach based on a convolutional neural network in order to create a new method for DTI prediction. Training results conducted with the Davis and KIBA datasets show that MPS2IT-DTI is viable compared to other state-of-the-art (SOTA) approaches in terms of performance and complexity of the neural network model. With the Davis dataset, we obtained 0.876 for the concordance index and 0.276 for the MSE; with the KIBA dataset, we obtained 0.836 and 0.226 for the concordance index and the MSE, respectively. Moreover, the MPS2IT-DTI model represents molecule and protein sequences as images, instead of treating them as an NLP task, and as such, does not employ an embedding layer, which is present in other models. [ABSTRACT FROM AUTHOR]