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We investigated the properties of the low molecular weight thermo-alkali-stable and mercury ion-tolerant xylanase production from Thermomyces dupontii KKU-CLD-E2-3. The xylanase was purified to homogeneity by ammonium sulfate, Sephadex G-100 and DEAE-cellulose column chromatography which resulted 27.92-fold purification specific activity of 56.19 U/mg protein and a recovery yield of 2.01%. The purified xylanase showed a molecular weight of 25 kDa by SDS-PAGE and the partial peptide sequence showed maximum sequence homology to the endo-1,4-β-xylanase. The optimum temperature and pH for its activity were 80 °C and pH 9.0, respectively. Furthermore, the purified xylanase can maintain more than 75% of the original activity in pH range of 7.0-10.0 after incubation at 4 °C for 24 h, and can still maintain more than 70% of original activity after incubating at 70 °C for 90 min. Our purified xylanase was activated by Cu 2+ and Hg 2+ up to 277% and 235% of initial activity, respectively but inhibited by Co 2+ , Ag + and SDS at a concentration of 5 mM. The K m and V max values of beechwood xylan were 3.38 mg/mL and 625 µmol/min/mg, respectively. Furthermore, our xylanase had activity specifically to xylan-containing substrates and hydrolyzed beechwood xylan, and the end products mainly were xylotetraose and xylobiose. The results suggested that our purified xylanase has potential to use for pulp bleaching in the pulp and paper industry.

A single amino acid in a peptide sequence can play an important role to tune the self‐assembly and hydrogelation behaviour. Here, a C‐terminal cysteine‐containing ultrashort peptide hydrogelator forms hydrogel through non‐covalent and covalent interactions. Interestingly, the hydrogel is insoluble in water and buffer solutions at different pH values (1‐13) and is thixotropic and injectable. In recent years, removing dyes from contaminated water has become a significant concern because of the shortage of freshwater resources. Therefore, the adsorption of dyes through a reliable, straightforward, nontoxic, cheap, and environmentally friendly adsorbent has become a popular topic. Hence, the hydrogelator was exploited to remove organic dyes from wastewater, harnessing its applicability in the gel phase and solid supports (filter paper and cotton). [ABSTRACT FROM AUTHOR]

A novel xylanase gene xynBCA , encoding a polypeptide of 439 residues (XynBCA), was cloned from Caldicoprobacter algeriensis genome and recombinantly expressed in Escherichia coli BL21(DE3). The amino acid sequence analysis showed that XynBCA belongs to the glycoside hydrolase family 10. The purified recombinant enzyme has a monomeric structure of 52 kDa. It is active and stable in a wide range of pH from 3 to 10 with a maximum activity at 6.5. Interestingly, XynBCA was highly thermoactive with an optimum temperature of 80 °C, thermostable with a half-life of 20 min at 80 °C. The specific activity was 117 U mg−1, while the K m and V max were 1.247 mg ml−1, and 114.7 μmol min−1 mg−1, respectively. The investigation of XynBCA in kraft pulp biobleaching experiments showed effectiveness in releasing reducing sugars and chromophores, with best achievements at 100 U g−1 of pulp and 1 h of incubation. The comparative molecular modeling studies with the less thermostable Xylanase B from Clostridium stercorarium , revealed extra charged residues at the surface of XynBCA potentially participating in the formation of intermolecular hydrogen bonds with solvent molecules or generating salt bridges, therefore contributing to the higher thermal stability. [ABSTRACT FROM AUTHOR]

Thermoalkaliphilic GH11 xylanases are largely favored for paper pulp biobleaching process. The present work aimed to comparatively investigate the molecular phylogeny, amino acid sequences, molecular structure, and enzyme–substrate interaction of six thermoalkaliphilic GH11 xylanases from different bacterial species (Oxalobacteraceae bacterium xylanase = ObXyl, Sphingomonas sp. xylanase = SsXyl, Hymenobacter sp. xylanase = HsXyl, Amycolatopsis vastitatis xylanase = AvXyl, Lentzea deserti xylanase = LdXyl, Streptomyces rubellomurinus xylanase = SrXyl). For this purpose, six bacterial thermoalkaliphilic GH11 xylanase sequences derived from unreviewed protein entries of UniProt/TrEMBL database were analyzed for their phylogenetic relationships and sequence similarities. Also, 3D predicted structures of the enzymes were built and computationally validated by different bioinformatics tools. The enzyme–substrate interactions were investigated by molecular docking analysis using various substrates. Phylogenetic analysis showed that six enzymes were grouped into two different clusters: the first cluster included ObXyl, SsXyl, and HsXyl, whereas the second cluster had AvXyl, LdXyl, and SrXyl. Multiple sequence alignment showed that the second cluster xylanases possessed longer N-terminal regions indicating higher thermostability, compared to the first cluster xylanases. The structural analyses showed that six predicted structures were largely conserved. Molecular docking results indicated that binding efficiency to xylotriose, xylotetraose, and xylopentaose was higher in second cluster enzymes than that in first cluster enzymes, exhibiting mostly above -8.0 kCal/mol of binding energy. Arginine in B8 β-strand was commonly involved in substrate interactions in all the second cluster xylanases, different from the first cluster ones. Thus, the present work predicted that the thermoalkaliphilic xylanases in the second cluster might be greater potential candidates for the paper pulp bleaching process. [ABSTRACT FROM AUTHOR]

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]

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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]

Abstract: Protein ORIGAMI ( http://ibg.kit.edu/protein_origami) is a browser‐based web application that allows the user to create straightforward 3D paper models of folded peptides for research, teaching and presentations. An amino acid sequence can be turned into α‐helices, β‐strands and random coils that can be printed out and folded into properly scaled models, with a color code denoting the biophysical characteristics of each amino acid residue (hydrophobicity, charge, etc.). These models provide an intuitive visual and tactile understanding of peptide interactions with other partners, such as helix‐helix assembly, oligomerization, membrane binding, or pore formation. Helices can also be displayed as a helical wheel or helical mesh in 2D graphics, to be used in publications or presentations. The highly versatile programme Protein ORIGAMI is also suited to create less conventional helices with arbitrary pitch (e.g., 310‐helix, π‐helix, or left‐handed helices). Noncanonical amino acids, labels and different terminal modifications can be defined and displayed at will, and different protonation states can be shown. In addition to the web application, the program source code can be downloaded and installed locally on a PC. The printed paper models can be readily used for daily research and discussions, just as for educational purposes and teaching. © 2018 by The International Union of Biochemistry and Molecular Biology, 46:403–409, 2018. [ABSTRACT FROM AUTHOR]

Background: The revolution in molecular biology has shown how protein function and structure are based on specific sequences of amino acids. Thus, an important feature in many papers is the mention of the significance of individual amino acids in the context of the entire sequence of the protein. MutationFinder is a widely used program for finding mentions of specific mutations in texts. We report on augmenting the positive attributes of MutationFinder with a more inclusive regular expression list to create ResidueFinder, which finds mentions of native amino acids as well as mutations. We also consider parameter options for both ResidueFinder and MutationFinder to explore trade-offs between precision, recall, and computational efficiency. We test our methods and software in full text as well as abstracts. Results: We find there is much more variety of formats for mentioning residues in the entire text of papers than in abstracts alone. Failure to take these multiple formats into account results in many false negatives in the program. Since MutationFinder, like several other programs, was primarily tested on abstracts, we found it necessary to build an expanded regular expression list to achieve acceptable recall in full text searches. We also discovered a number of artifacts arising from PDF to text conversion, which we wrote elements in the regular expression library to address. Taking into account those factors resulted in high recall on randomly selected primary research articles. We also developed a streamlined regular expression (called "cut") which enables a several hundredfold speedup in both MutationFinder and ResidueFinder with only a modest compromise of recall. All regular expressions were tested using expanded F-measure statistics, i.e., we compute Fβ for various values of where the larger the value of β the more recall is weighted, the smaller the value of β the more precision is weighted. Conclusions: ResidueFinder is a simple, effective, and efficient program for finding individual residue mentions in primary literature starting with text files, implemented in Python, and available in SourceForge.net. The most computationally efficient versions of ResidueFinder could enable creation and maintenance of a database of residue mentions encompassing all articles in PubMed. [ABSTRACT FROM AUTHOR]

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]

Purpose: Sequenced Protein–Protein Interaction (PPI) prediction represents a pivotal area of study in biology, playing a crucial role in elucidating the mechanistic underpinnings of diseases and facilitating the design of novel therapeutic interventions. Conventional methods for extracting features through experimental processes have proven to be both costly and exceedingly complex. In light of these challenges, the scientific community has turned to computational approaches, particularly those grounded in deep learning methodologies. Despite the progress achieved by current deep learning technologies, their effectiveness diminishes when applied to larger, unfamiliar datasets. Results: In this study, the paper introduces a novel deep learning framework, termed DL-PPI, for predicting PPIs based on sequence data. The proposed framework comprises two key components aimed at improving the accuracy of feature extraction from individual protein sequences and capturing relationships between proteins in unfamiliar datasets. 1. Protein Node Feature Extraction Module: To enhance the accuracy of feature extraction from individual protein sequences and facilitate the understanding of relationships between proteins in unknown datasets, the paper devised a novel protein node feature extraction module utilizing the Inception method. This module efficiently captures relevant patterns and representations within protein sequences, enabling more informative feature extraction. 2. Feature-Relational Reasoning Network (FRN): In the Global Feature Extraction module of our model, the paper developed a novel FRN that leveraged Graph Neural Networks to determine interactions between pairs of input proteins. The FRN effectively captures the underlying relational information between proteins, contributing to improved PPI predictions. DL-PPI framework demonstrates state-of-the-art performance in the realm of sequence-based PPI prediction. [ABSTRACT FROM AUTHOR]

Ferredoxin (FDX) is a highly conserved iron-sulfur protein that participates in redox reactions and plays an important role as an electron transport protein in biological processes. However, its function in marine fish remains unclear. We identified two ferrodoxin proteins, FDX1 and FDX2, from black scraper (Thamnaconus modestus) to confirm their genetic structures and expression profiles and to investigate their antimicrobial activity properties by fabricating them with antimicrobial peptides based on sequences. The two TmFDXs mRNAs were most abundant in peripheral blood leukocytes of healthy T. modestus. After artificial infection with Vibrio anguillarum, a major pathogen of T. modestus, TmFDX1 mRNA was significantly upregulated in the gills, heart, intestines, kidneys, liver, and spleen, but was consistently downregulated in the brain. The expression levels of TmFDX2 mRNA were significantly upregulated in the heart, intestines, kidneys, liver, and spleen; however, no significant changes in expression were observed in the brain or gills. Based on the 2Fe-2S ferredoxin-type iron-sulfur-binding domain sequence, two peptides (pFDX1 and pFDX2) were synthesized. The bactericidal effect, biofilm formation inhibition, and gDNA-binding activity of these peptides were investigated. These findings highlight the potential as a natural peptide candidate for TmFDXs., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

We identified a novel C-type lectin (CTL) from Macrobrachium nipponense, designated as Mn-clip-Lec. It consists of 1315 bp with an open reading frame of 1098 bp, encoding a polypeptide of 365 amino acids. Mn-clip-Lec contains 6 exons and 5 introns. Mn-clip-Lec possessed a CLIP domain at the N-terminal and two carbohydrate recognition domains at the C-terminal. Interaction between Mn-clip-Lec and MnLec was found by Yeast two-hybrid analysis. The expressions of Mn-clip-Lec, MnLec, prophenoloxidase (proPO)-activating system-associated genes (MnPPAF, MnPPAE, and MnPO), and antimicrobial peptides (AMPs) (MnALF and MnCRU) were up-regulated after the challenge with Staphylococcus aureus. RNA interference (RNAi)-mediated suppression of the Mn-clip-Lec and MnLec genes in S. aureus-challenged prawns reduced the transcripts of MnPPAF, MnPPAE, MnPO, MnALF and MnCRU. Knockdown of Mn-clip-Lec and MnLec resulted in decrease in PO activity in M. nipponense infected with S. aureus. The recombinant Mn-clip-Lec (rMn-clip-Lec) protein bound all tested bacteria and agglutinated S. aureus. A sugar-binding assay revealed that rMn-clip-Lec could bind to LPS or PGN. rMn-clip-Lec accelerated the clearance of S. aureus in vivo. Our findings suggest that Mn-clip-Lec and its interacting MnLec play important roles in the induction of the proPO system and AMPs expression in M. nipponense during bacterial infection., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Myeloid differentiation factor-88 (MyD88) is a key adaptor of the toll-like receptor (TLR) signaling pathway and plays a crucial role in innate immune signal transduction in animals. However, the MyD88-mediated signal transduction mechanism in shellfish has not been well studied. In this study, a new MyD88 gene, CfMyD88-2, was identified in the Zhikong scallop, Chlamys farreri. The 1779 bp long open reading frame encodes 592 amino acids. The N-terminus of CfMyD88-2 contains a conserved death domain (DD), followed by a TIR (TLR/Interleukin-1 Receptor) domain. The results of the multi-sequence comparison showed that the TIR domain sequences were highly conserved. Phylogenetic analysis revealed that CfMyD88-2 was first associated with Mizuhopecten yessoensis MyD88-4 and Argopecten irradians MyD88-4. CfMyD88-2 mRNA was expressed in all scallop tissues, as detected by qRT-PCR, and the expression level was the highest in the mantle and hepatopancreas. In addition, CfMyD88-2 mRNA expression significantly increased after pathogen-associated molecular patterns (PAMPs, such as lipopolysaccharide, peptidoglycan, or polyinosinic-polycytidylic acid) stimulation. The results of the co-immunoprecipitation experiments in HEK293T cells showed that both CfMyD88-1 and CfMyD88-2 interacted with the TLR protein of scallops, suggesting the existence of more than one functional TLR-MyD88 signaling axis in scallops. Dual luciferase reporter gene assays indicated that the overexpressed CfMyD88-2 in HEK293T cells activated interferon (IFN) α, IFN-β, IFN-γ, and NF-κB reporter genes, indicating that the protein has multiple functions. The results of the subcellular localization experiment uncovered that CfMyD88-2 was mainly localized in the cytoplasm of human cells. In summary, the novel identified CfMyD88-2 can respond to the challenge of PAMPs, participate in TLR immune signaling, and may activate downstream effector genes such as NF-κB gene. These research results will be useful in advancing the theory of innate immunity in invertebrates and provide a reference for the selection of disease-resistant scallops in the future., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

Intracellular antioxidant glutaredoxin controls cell proliferation and survival. Based on the active site, structure, and conserved domain motifs, it is classified into two classes. Class I contains dithiol Grxs with two cysteines in the consensus active site sequence CXXC, while class II has monothiol Grxs with one cysteine residue in the active site. Monothiol Grxs can also have an additional N-terminal thioredoxin (Trx)-like domain. Previously, we reported the characterization of Grx1 from Hydra vulgaris (HvGrx1), which is a dithiol isoform. Here, we report the molecular cloning, expression, analysis, and characterization of another isoform of Grx, which is the multidomain monothiol glutaredoxin-3 from Hydra vulgaris (HvGrx3). It encodes a protein with 303 amino acids and is significantly larger and more divergent than HvGrx1. In-silico analysis revealed that Grx1 and Grx3 have 22.5% and 9.9% identical nucleotide and amino acid sequences, respectively. HvGrx3 has two glutaredoxin domains and a thioredoxin-like domain at its amino terminus, unlike HvGrx1, which has a single glutaredoxin domain. Like other monothiol glutaredoxins, HvGrx3 failed to reduce glutathione-hydroxyethyl disulfide. In the whole Hydra, HvGrx3 was found to be expressed all over the body column, and treatment with H 2 O 2 led to a significant upregulation of HvGrx3. When transfected in HCT116 (human colon cancer cells) cells, HvGrx3 enhanced cell proliferation and migration, indicating that this isoform could be involved in these cellular functions. These transfected cells also tolerate oxidative stress better., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

NIK (NF-κB inducing kinase) belongs to the mitogen-activated protein kinase family, which activates NF-κB and plays a vital role in immunology, inflammation, apoptosis, and a series of pathological responses. In NF-κB noncanonical pathway, NIK and IKKα have been often studied in mammals and zebrafish. However, few have explored the relationship between NIK and other subunits of the IKK complex. As a classic kinase in the NF-κB canonical pathway, IKKβ has never been researched with NIK in fish. In this paper, the full-length cDNA sequence of grass carp (Ctenopharyngodon idella) NIK (CiNIK) was first cloned and identified. The expression level of CiNIK in grass carp cells was increased under GCRV stimuli. Under the stimulation of GCRV, poly (I:C), and LPS, the expression of NIK in various tissues of grass carp was also increased. This suggests that CiNIK responds to viral stimuli. To study the relationship between CiNIK and CiIKKβ, we co-transfected CiNIK-FLAG and CiIKKB-GFP into grass carp cells in coimmunoprecipitation and immunofluorescence experiments. The results revealed that CiNIK interacts with CiIKKβ. Besides, the degree of autophosphorylation of CiNIK was enhanced under poly (I:C) stimulation. CiIKKβ was phosphorylated by CiNIK and then activated the activity of p65. The activity change of p65 indicates that NF-κB downstream inflammatory genes will be functioning. CiNIK or CiIKKβ up-regulated the expression of IL-8. It got higher when CiNIK and CiIKKβ coexisted. This paper revealed that NF-κB canonical pathway and noncanonical pathway are not completely separated in generating benefits., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Protein structure prediction (PSP) is a grand challenge in bioinformatics, drug discovery, and related fields. PSP is computationally challenging because of an astronomically large conformational space to be searched and an unknown very complex energy function to be minimised. To obtain a given protein's structure, template-based PSP approaches adopt a similar protein's known structure, while template-free PSP approaches work when no similar protein's structure is known. Currently, proteins with known structures are greatly outnumbered by proteins with unknown structures. Template-free PSP has obtained significant progress recently via machine learning and search-based optimisation approaches. However, very accurate structures for complex proteins are yet to be achieved at a level suitable for effective drug design. Moreover, ab initio prediction of a protein's structure only from its amino acid sequence remains unsolved. Furthermore, the number of protein sequences with unknown structures is growing rapidly. Hence, to make further progress in PSP, more sophisticated and advanced artificial intelligence (AI) approaches are needed. However, getting involved in PSP research is difficult for AI researchers because of the lack of a comprehensive understanding of the whole problem, along with the background and the literature of all related sub-problems. Unfortunately, existing PSP review papers cover PSP research at a very high level and only some parts of PSP and only from a particular singular viewpoint. Using a systematic approach, this review paper provides a comprehensive survey of the state-of-the-art template-free PSP research to fill this knowledge gap. Moreover, covering required PSP preliminaries and computational formulations, this paper presents PSP research from AI perspectives, discusses the challenges, provides our commentaries, and outlines future research directions. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) are currently among the most widely-used deep neural network (DNN) architectures available and achieve state-of-the-art performance for many problems. Originally applied to computer vision tasks, CNNs work well with any data with a spatial relationship, besides images, and have been applied to different fields. However, recent works have highlighted numerical stability challenges in DNNs, which also relates to their known sensitivity to noise injection. These challenges can jeopardise their performance and reliability. This paper investigates DeepGOPlus, a CNN that predicts protein function. DeepGOPlus has achieved state-of-the-art performance and can successfully take advantage and annotate the abounding protein sequences emerging in proteomics. We determine the numerical stability of the model's inference stage by quantifying the numerical uncertainty resulting from perturbations of the underlying floating-point data. In addition, we explore the opportunity to use reduced-precision floating point formats for DeepGOPlus inference, to reduce memory consumption and latency. This is achieved by instrumenting DeepGOPlus' execution using Monte Carlo Arithmetic, a technique that experimentally quantifies floating point operation errors and VPREC, a tool that emulates results with customizable floating point precision formats. Focus is placed on the inference stage as it is the primary deliverable of the DeepGOPlus model, widely applicable across different environments. All in all, our results show that although the DeepGOPlus CNN is very stable numerically, it can only be selectively implemented with lower-precision floating-point formats. We conclude that predictions obtained from the pre-trained DeepGOPlus model are very reliable numerically, and use existing floating-point formats efficiently. [ABSTRACT FROM AUTHOR]

Insects are the largest group of animals when it comes to the number and diversity of species. Yet, with the exception of Drosophila , no information is currently available on the primary structure of their sperm nuclear basic proteins (SNBPs). This paper represents the first attempt in this regard and provides information about six species of Neoptera: Poecillimon thessalicus, Graptosaltria nigrofuscata, Apis mellifera, Nasonia vitripennis, Parachauliodes continentalis , and Tribolium castaneum . The SNBPs of these species were characterized by acetic acid urea gel electrophoresis (AU-PAGE) and high-performance liquid chromatography fractionated. Protein sequencing was obtained using a combination of mass spectrometry sequencing, Edman N-terminal degradation sequencing and genome mining. While the SNBPs of several of these species exhibit a canonical arginine-rich protamine nature, a few of them exhibit a protamine-like composition. They appear to be the products of extensive cleavage processing from a precursor protein which are sometimes further processed by other post-translational modifications that are likely involved in the chromatin transitions observed during spermiogenesis in these organisms., Competing Interests: All authors have read and agreed to the published version of the manuscript and there is no conflict of interest, including specific financial interest and relationships and affiliations relevant to the subject of the manuscript.

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]

Most of the current nonclassical proteins prediction methods involve manual feature selection, such as constructing features of samples based on the physicochemical properties of proteins and position‐specific scoring matrix (PSSM). However, these tasks require researchers to perform some tedious search work to obtain the physicochemical properties of proteins. This paper proposes an end‐to‐end nonclassical secreted protein prediction model based on deep learning, named DeepNCSPP, which employs the protein sequence information and sequence statistics information as input to predict whether it is a nonclassical secreted protein. The protein sequence information and sequence statistics information are extracted using bidirectional long‐ and short‐term memory and convolutional neural networks, respectively. Among the experiments conducted on the independent test dataset, DeepNCSPP achieved excellent results with an accuracy of 88.24%, Matthews coefficient (MCC) of 77.01%, and F1‐score of 87.50%. Independent test dataset testing and 10‐fold cross‐validation show that DeepNCSPP achieves competitive performance with state‐of‐the‐art methods and can be used as a reliable nonclassical secreted protein prediction model. A web server has been constructed for the convenience of researchers. The web link is https://www.deepncspp.top/. The source code of DeepNCSPP has been hosted on GitHub and is available online (https://github.com/xiaoliu166370/DEEPNCSPP). This paper proposes an end‐to‐end nonclassical secreted protein prediction model DeepNCSPP based on deep learning, which employs the protein sequence information and sequence statistics information as input to predict whether it is a nonclassical secreted protein. The protein sequence information and sequence statistics information are extracted using bidirectional long‐ and short‐term memory and convolutional neural networks, respectively. [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]

Secondary structure prediction is a key step in understanding protein function and biological properties and is highly important in the fields of new drug development, disease treatment, bioengineering, etc. Accurately predicting the secondary structure of proteins helps to reveal how proteins are folded and how they function in cells. The application of deep learning models in protein structure prediction is particularly important because of their ability to process complex sequence information and extract meaningful patterns and features, thus significantly improving the accuracy and efficiency of prediction. In this study, a combined model integrating an improved temporal convolutional network (TCN), bidirectional long short-term memory (BiLSTM), and a multi-head attention (MHA) mechanism is proposed to enhance the accuracy of protein prediction in both eight-state and three-state structures. One-hot encoding features and word vector representations of physicochemical properties are incorporated. A significant emphasis is placed on knowledge distillation techniques utilizing the ProtT5 pretrained model, leading to performance improvements. The improved TCN, achieved through multiscale fusion and bidirectional operations, allows for better extraction of amino acid sequence features than traditional TCN models. The model demonstrated excellent prediction performance on multiple datasets. For the TS115, CB513 and PDB (2018–2020) datasets, the prediction accuracy of the eight-state structure of the six datasets in this paper reached 88.2%, 84.9%, and 95.3%, respectively, and the prediction accuracy of the three-state structure reached 91.3%, 90.3%, and 96.8%, respectively. This study not only improves the accuracy of protein secondary structure prediction but also provides an important tool for understanding protein structure and function, which is particularly applicable to resource-constrained contexts and provides a valuable tool for understanding protein structure and function. [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]

Summary Multiple sequence alignment is an important problem in computational biology with applications that include phylogeny and the detection of remote homology between protein sequences. UPP is a popular software package that constructs accurate multiple sequence alignments for large datasets based on ensembles of hidden Markov models (HMMs). A computational bottleneck for this method is a sequence-to-HMM assignment step, which relies on the precise computation of probability scores on the HMMs. In this work, we show that we can speed up this assignment step significantly by replacing these HMM probability scores with alternative scores that can be efficiently estimated. Our proposed approach utilizes a multi-armed bandit algorithm to adaptively and efficiently compute estimates of these scores. This allows us to achieve similar alignment accuracy as UPP with a significant reduction in computation time, particularly for datasets with long sequences. Availability and implementation The code used to produce the results in this paper is available on GitHub at: https://github.com/ilanshom/adaptiveMSA. [ABSTRACT FROM AUTHOR]