Heterologous biosynthesis of myxobacterial lanthipeptides melittapeptins

The myxobacteria are an attractive bioresource for bioactive compounds since the large size genome contains many biosynthetic gene clusters of secondary metabolites. The genome of the myxobacterium Melittangium boletus contains three biosynthetic gene clusters for lanthipeptide production. One of the gene clusters includes genes coding lanthipeptide precursor (melA), class II lanthipeptide synthetase (melM), and transporter (melT). The amino acid sequence of melA indicated similarity with that of known lanthipeptides mersacidin and lichenicidin A1 by the alignment. To perform heterologous production of new lanthipeptides, the expression vector containing the essential genes (melA and melM) was constructed by utilizing codon-optimized synthetic genes. The co-expression of two genes in the host bacterial cells of Escherichia coli BL21 (DE3) afforded new lanthipeptides named melittapeptins A-C. The structures of melittapeptins A-C including lanthionine/methyllanthionine bridge pattern were proposed based on protease digestion and MS/MS experiments. The native strain of M. boletus did not produce melittapeptins A-C, so heterologous production using the biosynthetic gene cluster was effective in obtaining the lanthipeptides. Melittapeptins A-C showed specific and potent antibacterial activity to the Gram-positive bacterium Micrococcus luteus. To the best of our knowledge, this is the first report of antibacterial lanthipeptides derived from myxobacterial origin. KEY POINTS: • New lanthipeptides melittapeptins were heterologously produced in Escherichia coli. • Melittapeptins showed specific antibacterial activity against Micrococcus luteus. • Melittapeptins were the first antibacterial lanthipeptides of myxobacterial origin.

Identification and semi-quantification of protein allergens in complex mixtures using proteomic and AllerCatPro 2.0 bioinformatic analyses: a proof-of-concept investigation

The demand for botanicals and natural substances in consumer products has increased in recent years. These substances usually contain proteins and these, in turn, can pose a risk for immunoglobulin E (IgE)-mediated sensitization and allergy. However, no method has yet been accepted or validated for assessment of potential allergenic hazards in such materials. In the studies here, a dual proteomic-bioinformatic approach is proposed to evaluate holistically allergenic hazards in complex mixtures of plants, insects, or animal proteins. Twelve commercial preparations of source materials (plant products, dust mite extract, and preparations of animal dander) known to contain allergenic proteins were analyzed by label-free proteomic analyses to identify and semi-quantify proteins. These were then evaluated by bioinformatics using AllerCatPro 2.0 (https://allercatpro.bii.a-star.edu.sg/) to predict no, weak, or strong evidence for allergenicity and similarity to source-specific allergens. In total, 4,586 protein sequences were identified in the 12 source materials combined. Of these, 1,665 sequences were predicted with weak or strong evidence for allergenic potential. This first-tier approach provided top-level information about the occurrence and abundance of proteins and potential allergens. With regards to source-specific allergens, 129 allergens were identified. The sum of the relative abundance of these allergens ranged from 0.8% (lamb's quarters) to 63% (olive pollen). It is proposed here that this dual proteomic-bioinformatic approach has the potential to provide detailed information on the presence and relative abundance of allergens, and can play an important role in identifying potential allergenic hazards in complex protein mixtures for the purposes of safety assessments.

Mining and rational design of psychrophilic catalases using metagenomics and deep learning models

A complete catalase-encoding gene, designated soiCat1, was obtained from soil samples via metagenomic sequencing, assembly, and gene prediction. soiCat1 showed 73% identity to a catalase-encoding gene of Mucilaginibacter rubeus strain P1, and the amino acid sequence of soiCAT1 showed 99% similarity to the catalase of a psychrophilic bacterium, Pedobacter cryoconitis. soiCAT1 was identified as a psychrophilic enzyme due to the low optimum temperature predicted by the deep learning model Preoptem, which was subsequently validated through analysis of enzymatic properties. Experimental results showed that soiCAT1 has a very narrow range of optimum temperature, with maximal specific activity occurring at the lowest test temperature (4 °C) and decreasing with increasing reaction temperature from 4 to 50 °C. To rationally design soiCAT1 with an improved temperature range, soiCAT1 was engineered through site-directed mutagenesis based on molecular evolution data analyzed through position-specific amino acid possibility calculation. Compared with the wild type, one mutant, soiCAT1S205K, exhibited an extended range of optimum temperature ranging from 4 to 20 °C. The strategies used in this study may shed light on the mining of genes of interest and rational design of desirable proteins. KEY POINTS: • Numerous putative catalases were mined from soil samples via metagenomics. • A complete sequence encoding a psychrophilic catalase was obtained. • A mutant psychrophilic catalase with an extended range of optimum temperature was engineered through site-directed mutagenesis.

Exploring the roles of excess amino acids, creatine, creatinine, and glucose in the formation of heterocyclic aromatic amines by UPLC-MS/MS

The prevention and control of heterocyclic aromatic amines (HAA) formation to mitigate of potential risks to humans, can be achieved by targeting their precursors. In this study, the detailed roles of individual and excess component (20 common α-amino acids, creatine, creatinine, and glucose) on HAA formation in roasted beef patties were examined using UPLC-MS/MS. The results confirmed the reported classical precursors of HAAs. Some components regulated the competitive production of Norharman and Harman. Glycine (Gly) and glucose favored Norharman formation, while cysteine (Cys) and phenylalanine (Phe) for Harman. Serine (Ser) and threonine (Thr) were identified as potential precursors for IQx-type HAAs. Interestingly, methionine (Met), Gly, Thr, Cys, alanine (Ala), and Ser were revealed as more targeted underlying precursors for 1,6-DMIP and 1,5,6-TMIP, and the formation mechanism was inferred. Furthermore, Pro, Leu, His, Ile, Lys and Asp were considered as great inhibitors for HAAs.

High-throughput method for Peptide mapping and Amino acid sequencing for Calcitonin Salmon in Calcitonin Salmon injection using Ultra High Performance Liquid Chromatography - High Resolution Mass Spectrometry (UHPLC-HRMS) with the application of Bioinformatic tools

BACKGROUND

Tandem mass spectrometry (MS/MS) can provide direct and accurate sequence characterization of synthetic peptide drugs, and peptide drug products including side chain modifications in the Peptide drugs. This article explains a step-by-step guide to developing a high-throughput method using high resolution mass spectrometry for characterization of Calcitonin Salmon injection containing high proportion of UV-active excipients.

METHODS

The major challenge in the method development of Amino acid sequencing and Peptide mapping was presence of phenol in drug product. Phenol is a UV-active excipient and reacts with both Dithiothreitol (DTT) and Trypsin. Hence Calcitonin Salmon was extracted from the Calcitonin Salmon injection using solid phase extraction after the extraction, Amino acid sequencing and peptide mapping study was performed. Upon incubation of Calcitonin Salmon with Trypsin and DTT, digested fragments were generated which were separated by mass compatible reverse phase chromatography and the molecular mass of each fragment was determined using HRMS.

RESULTS

A reverse phase chromatographic method was developed using UHPLC-HRMS for the determination of direct mass, peptide mapping and to determine the amino acid sequencing in the Calcitonin Salmon injection. The method was found Specific and fragments after trypsin digest are well resolved from each other and the molecular mass of each fragment was determined using HRMS. Sequencing was performed using automated identification of b and y ions annotation and identifications based on MS/MS spectra using Biopharma finder and Proteome discoverer software.

CONCLUSION

Using this approach 100% protein coverage was obtained and protein was identified as Calcitonin Salmon and the observed masses of tryptic digest of peptide was found similar with theoretical masses. The method can be used for both UV and MS based Peptide mapping and whereas the UV based peptide mapping method can be used as identification test for Calcitonin Salmon drug substance and drug product in quality control.

Reverse-engineering the anti-MUC1 antibody 139H2 by mass spectrometry-based de novo sequencing

Mucin 1 (MUC1) is a transmembrane mucin expressed at the apical surface of epithelial cells at mucosal surfaces. MUC1 has a barrier function against bacterial invasion and is well known for its aberrant expression and glycosylation in adenocarcinomas. The MUC1 extracellular domain contains a variable number of tandem repeats (VNTR) of 20 amino acids, which are heavily O-linked glycosylated. Monoclonal antibodies against the MUC1 VNTR are powerful research tools with applications in the diagnosis and treatment of MUC1-expressing cancers. Here, we report direct mass spectrometry-based sequencing of anti-MUC1 hybridoma-derived 139H2 IgG, enabling reverse-engineering of the functional recombinant monoclonal antibody. The crystal structure of the 139H2 Fab fragment in complex with the MUC1 epitope was solved, revealing the molecular basis of 139H2 binding specificity to MUC1 and its tolerance to O-glycosylation of the VNTR. The available sequence of 139H2 will allow further development of MUC1-related diagnostic, targeting, and treatment strategies.

Functional insights of Tyr37 in framework region 2 directly contributing to the binding affinities and dissociation kinetics in single-domain VHH antibodies

Single-domain VHH antibody is regarded as one of the promising antibody classes for therapeutic and diagnostic applications. VHH antibodies have amino acids in framework region 2 that are distinct from those in conventional antibodies, such as the Val37Phe/Tyr (V37F/Y) substitution. Correlations between the residue type at position 37 and the conformation of the CDR3 in VHH antigen recognition have been previously reported. However, few studies focused on the meaning of harboring two residue types in position 37 of VHH antibodies, and the concrete roles of Y37 have been little to be elucidated. Here, we investigated the functional states of position 37 in co-crystal structures and performed analyses of three model antibodies with either F or Y at position 37. Our analysis indicates that Y at position 37 enhances the dissociation rate, which is highly correlated with drug efficacy. Our findings help to explain the molecular mechanisms that distinguish VHH antibodies from conventional antibodies.

Effects of intrinsically disordered regions in gp120 underlying HIV neutralization phenotypes

HIV envelope protein gp120 is considered a primary molecular determinant of viral neutralization phenotype due to its critical role in viral entry and immune evasion. The intrinsically disordered regions (IDRs) in gp120 are responsible for their extensive sequence variations and significant structural rearrangements. Despite HIV neutralization phenotype and sequence/structural information of gp120 have been experimentally characterized, there remains a gap in our understanding of the correlation between the viral phenotype and IDRs in gp120. Here, we combined machine learning (ML) techniques and molecular dynamics (MD) simulations to gain data-driven and molecule-mechanism insights into relationships between viral sequence, structure, and phenotypes from the perspective of IDRs in gp120. ML models, trained only on the length and disorder score of IDRs, achieved equivalent performance to the best baseline model using amino acid sequences to discriminate HIV neutralization phenotype, indicating that the lengths or disorder of specific IDRs are strongly related to HIV neutralization phenotypes. Comparative MD analysis reveals that gp120 with extreme neutralization phenotypes in multiple conformational states, especially some IDRs, exhibit significantly distinct structural dynamics, conformational flexibility, and thermodynamic distributions. Taken together, our study provided insights into the role of IDRs in gp120 responding to HIV neutralization phenotypes, which will advance the understanding of molecular mechanisms underlying viral function associated with HIV neutralization phenotype and help develop antiviral vaccines or drugs.

Ectopic expression of the Arabidopsis mutant L3 NB-LRR receptor gene in Nicotiana benthamiana cells leads to cell death

Nucleotide-binding sites and leucine-rich repeat proteins (NLRs) act as critical intracellular immune receptors. Previous studies reported an Arabidopsis-resistant gene L3 (AT1G15890), which encoded a coiled-coil (CC) NLR that conferred cell death in bacteria; however, its function in planta remains unclear. This study describes a comprehensive structure-function analysis of L3 in Nicotiana benthamiana. The results of the transient assay showed that the L3 CC domain is sufficient for cell-death induction. The first 140 amino acid segment constituted the minimal function region that could cause cell death. The YFP-labeled L3 CC domain was localized to the plasma membrane, which was considered crucial for the function and self-interaction of the L3 CC domain. The results of point mutations analysis showed that L3 CC domain function is affected by mutations in some specific residues, and loss-of-function mutations in the CC domain affected the function of full-length L3. These study results offered considerable evidence to understand the activation mechanism of L3.

Structure of a phosphodiesterase from Streptomyces sanglieri with a novel C-terminal domain

A glycerophosphoethanolamine ethanolaminephosphodiesterase (GPE-EP) from Streptomyces sanglieri hydrolyzes glycerophosphoethanolamine to phosphoethanolamine and glycerol. The structure of GPE-EP was determined by the molecular replacement method using a search model generated with AlphaFold2. This structure includes the entire length of the mature protein and it is composed of an N-terminal domain and a novel C-terminal domain connected to a flexible linker. The N-terminal domain is the catalytic domain containing calcium ions at the catalytic site. Coordination bonds were observed between five amino acid residues and glycerol. Although the function of the C-terminal domain is currently unknown, inter-domain interactions between the N- and C-terminal domains may contribute to its relatively high thermostability.

Domain of unknown function (DUF) proteins in plants: function and perspective

Domains of unknown function (DUFs), which are deposited in the protein family database (Pfam), are protein domains with conserved amino acid sequences and uncharacterized functions. Proteins with the same DUF were classified as DUF families. Although DUF families are generally not essential for the survival of plants, they play roles in plant development and adaptation. Characterizing the functions of DUFs is important for deciphering biological puzzles. DUFs were generally studied through forward and reverse genetics. Some novelty approaches, especially the determination of crystal structures and interaction partners of the DUFs, should attract more attention. This review described the identification of DUF genes by genome-wide and transcriptome-wide analyses, summarized the function of DUF-containing proteins, and addressed the prospects for future studies in DUFs in plants.

Predicting the functional state of protein kinases using interpretable graph neural networks from sequence and structural data

Protein kinases are central to cellular activities and are actively pursued as drug targets for several conditions including cancer and autoimmune diseases. Despite the availability of a large structural database for kinases, methodologies to elucidate the structure-function relationship of these proteins (without manual intervention) are lacking. Such techniques are essential in structural biology and to accelerate drug discovery efforts. Here, we implement an interpretable graph neural network (GNN) framework for classifying the functionally active and inactive states of a large set of protein kinases by only using their tertiary structure and amino acid sequence. We show that the GNN models can classify kinase structures with high accuracy (>97%). We implement the Gradient-weighted Class Activation Mapping for graphs (Graph Grad-CAM) to automatically identify structurally important residues and residue-residue contacts of the kinases without any a priori input. We show that the motifs identified through the Graph Grad-CAM methodology are functionally critical, consistent with the existing kinase literature. Notably, the highly conserved DFG and HRD motifs of the well-known hydrophobic spine are identified by the interpretable framework in addition to some of the lesser known motifs. Further, using Grad-CAM maps as the vector embedding of the protein structures, we identify the subtle differences in the crystal structures among different sub-classes of kinases in the Protein Data Bank (PDB). Frameworks such as the one implemented here, for high-throughput identification of protein structure-function relationships are essential in designing targeted small molecules therapies as well as in engineering new proteins for novel applications.

The antiviral role of largemouth bass STING against iridovirus infection

Stimulator of interferon gene (STING) plays a crucial role in the innate immune response against viral and bacterial pathogens. However, its function in largemouth bass iridovirus (LMBV) infection remains uncertain. Here, a STING homolog (MsSTING) from largemouth bass (Micropterus salmoides) was cloned and characterized. MsSTING encoded a 407-amino-acid polypeptide, which shared 84.08% and 41.45% identity with golden perch (Perca flavescens) and human (Homo sapiens) homologs, respectively. MsSTING contained four transmembrane domains and a conserved C-terminal domain. The mRNA level of MsSTING was significantly increased in response to LMBV infection in vitro. Subcellular localization observation indicated that MsSTING encoded a cytoplasmic protein, which co-localized predominantly with endoplasmic reticulum (ER) and partially with mitochondria. Moreover, its accurate localization was dependent on the N-terminal transmembrane motif (TM) domains. MsSTING was able to activate interferon (IFN) response, evidenced by the activation of IFN1, IFN3 and ISRE promoters by its overexpression in vitro. Mutant analysis showed that both the N-terminal and C-terminal domain of MsSTING were essential for its activation on IFN response. In addition, overexpression of MsSTING inhibited the transcription and protein levels of viral core genes, indicating that MsSTING exerted antiviral action against LMBV. Consistently, the inhibitory effects were significantly attenuated when the N-terminal or C-terminal domains of MsSTING was deleted. Furthermore, MsSTING overexpression upregulated the transcriptions of interferon-related genes and pro-inflammatory factors, including TANK-binding kinase 1(TBK1), interferon regulatory factor 3 (IRF3), interferon regulatory factor 7 (IRF7), interferon stimulated exonuclease gene 20 (ISG20), interferon-induced transmembrane protein 1(IFITM1), interferon γ (IFN-γ), tumor necrosis factor α (TNF-α), interleukin 1β (IL-1β), and interleukin 6 (IL-6). Together, MsSTING exerted antiviral action upon LMBV infection through positive regulation the innate immune response.

Characterization of a novel Type-I Crustin (carcininPm2) from black tiger shrimp Penaeus monodon

Carcinins are type-I crustins from crustaceans and play an important role in innate immune system. In this study, type-I crustins, carcininPm1 and carcininPm2, from the hemocytes of Penaeus monodon were identified. Comparison of their amino acid sequences and the phylogenetic tree revealed that they were closely related to the other crustacean carcinin proteins, but were clustered into different groups of the carcinin proteins. The full-length amino acids of carcininPm1 and carcininPm2 were 92 and 111 residues, respectively. CarcininPm1 and carcininPm2 were expressed mainly in hemocytes and intestine compared to the other tissues. The expression of carcininPm1 and carcininPm2 were dramatically increased in early time of bacterial challenged shrimp hemocytes. In contrast, the carcininPm1 and carcininPm2 were expressed in response to late state of YHV-infected shrimp hemocytes where the copy number of virus was high. The recombinant carcininPm2 (rcarcininPm2) but not its WAP domain (rcarcininPm2_WAP) exhibited antimicrobial activity against Vibrio harveyi and Vibrio parahaemolyticus AHPND but not other bacteria tested. The rcarcininPm2 was able to prolong the survival rate of VH-treated post larval shrimp from about 102 h to 156 h. These studies indicated that the carcininPm2 possessed the potential and challenges as antibacterial in innate immunity of shrimp.

The effects of four paralogous piscidin antimicrobial peptides on the chemotaxis, macrophage respiratory burst, phagocytosis and expression of immune-related genes in orange-spotted grouper (Epinephelus coicodes)

Antimicrobial peptides (AMPs) are an essential part of the vertebrate innate immune system. Piscidins are a family of AMPs specific in fish. In our previous investigation, we identified four paralogous genes of piscidins in the orange-spotted grouper (Epinephelus coicodes), which exhibited distinct activities against bacteria, fungi, and parasitic ciliated protozoa. Piscidins demonstrated their capability to modulate the expression of diverse immune-related genes; however, their precise immunoregulatory functions remain largely unexplored. In this study, we examined the immunomodulatory properties of putative mature peptides derived from four E. coicodes piscidins (ecPis1S, ecPis2S, ecPis3S, and ecPis4S) in head kidney leukocytes (HKLs) or monocytes/macrophages (MO/MΦ)-like cells isolated from E. coicodes. Our data demonstrate that E. coicodes piscidins exhibit immunomodulatory activities supported by multiple lines of evidence. Firstly, all four piscidins displayed chemotactic activities towards HKLs, with the most potent chemotactic activity observed in ecPis2S. Secondly, stimulation with E. coicodes piscidins enhanced respiratory burst and phagocytic activity in MO/MФ-like cells, with ecPis3S showing the highest efficacy in increasing phagocytosis of MO/MΦ-like cells. Thirdly, mRNA expression levels of chemokine receptors, Toll-like receptors, T cell receptors, and proinflammatory cytokines were modulated to varying extents by the four piscidins in E. coicodes HKLs. Overall, our findings indicate that the immunological activities of these four paralogous piscidins from E. coicodes are exhibited in a paralog-specific and concentration-dependent manner, highlighting their distinct and versatile immunomodulatory properties. This study makes a significant contribution to the field of fish AMPs immunology by elucidating the novel mechanisms through which members of the piscidin family exert their immunomodulatory effects. Moreover, it provides valuable insights for further exploration of fish immunomodulating agents.

Structural and biochemical characterization of active sites mutant in human inorganic pyrophosphatase

Inorganic pyrophosphatases (PPases) are enzymes that catalyze the conversion of inorganic pyrophosphate (PPi) into phosphate (Pi). Human inorganic pyrophosphatase 1 (Hu-PPase) exhibits high expression levels in a variety of tumors and plays roles in cell proliferation, apoptosis, invasion and metastasis, making it a promising prognostic biomarker and a target for cancer therapy. Despite its widespread presence, the catalytic mechanism of Hu-PPase in humans remains inadequately understood. The signature motif amino acid sequence (DXDPXD) within the active sites of PPases is preserved across different species. In this research, an enzymatic activity assay revealed that mutations led to a notable reduction in enzymatic function, although the impact of the four amino acids on the activity of the pocket varied. To investigate the influence of these residues on the substrate binding and enzymatic function of PPase, the crystal structure of the Hu-PPase-ED quadruple mutant (D116A/D118A/P119A/D121A) was determined at 1.69 Å resolution. The resulting structure maintained a barrel-like shape similar to that of the wild-type, albeit lacking Mg2+ ions. Molecular docking analysis demonstrated a decreased ability of Hu-PPase-ED to bind to PPi. Further, molecular dynamics simulation analysis indicated that the mutation rendered the loop of Mg2+ ion-binding residues less stable. Therefore, the effect on enzyme activity did not result from a change in the gross protein structure but rather from a mutation that abolished the Mg2+-coordinating groups, thereby eliminating Mg2+ binding and leading to the loss of enzyme activity.

A pattern recognition receptor interleukin-1 receptor is involved in reproductive immunity in Macrobrachium nipponense ovary

The family of TIR domain-containing receptors includes numerous proteins involved in innate immunity. In this study, a member of this family was characterized from the ovary of the oriental river prawn Macrobrachium nipponense and identified as interleukin-1 receptor (MnIL-1R). Meanwhile, to elucidate the conservation of IL-1R, its orthologous were identified in several crustacean species as well. In addition, the expression pattern of MnIL-1R in various adult tissues and post different pathogen-associated molecular patterns (PAMPs) challenge in ovary was analyzed with qRT-PCR technology. Finally, the roles of MnIL-1R in the ovary were analyzed by RNAi technology. The main results are as follows: (1) MnIL-1R comprises a 1785 bp ORF encoding 594 amino acids and is structurally composed of five domains: a signal peptide, two immunoglobulin (IG) domains, a transmembrane region, and a TIR-2 domain; (2) the TIR domain showed a high conservation among analyzed crustacean species; (3) MnIL-1R is widely detected in all tested tissues including ovary; (4) MnIL-1R showed a positive response to challenges with LPS, PGN, and polyI:C in the ovary; (5) its IG domain showed strong binding ability to LPS and PGN, confirming its role as a pattern recognition receptor; (6) the expression patterns of several members of the Toll signaling pathway (Myd88, TRAF-6, Dorsal, and Relish) was similar to that of MnIL-1R after challenges with LPS, PGN, and polyI:C in the ovary; (7) the silencing of MnIL-1R resulted in down-regulation of theses gene' (Myd88, TRAF-6, Dorsal, and Relish) expression level in the ovary. These results suggest that MnIL-1R can activate the Toll signaling pathway in the ovary by directly recognizing LPS and PGN through its IG domain, thereby contributing to the immune response in the ovary of M. nipponense.

In BTK, phosphorylated Y223 in the SH3 domain mirrors catalytic activity, but does not influence biological function

ABSTRACT

Bruton's tyrosine kinase (BTK) is an enzyme needed for B-cell survival, and its inhibitors have become potent targeted medicines for the treatment of B-cell malignancies. The initial activation event of cytoplasmic protein-tyrosine kinases is the phosphorylation of a conserved regulatory tyrosine in the catalytic domain, which in BTK is represented by tyrosine 551. In addition, the tyrosine 223 (Y223) residue in the SRC homology 3 (SH3) domain has, for more than 2 decades, generally been considered necessary for full enzymatic activity. The initial recognition of its potential importance stems from transformation assays using nonlymphoid cells. To determine the biological significance of this residue, we generated CRISPR-Cas-mediated knockin mice carrying a tyrosine to phenylalanine substitution (Y223F), maintaining aromaticity and bulkiness while prohibiting phosphorylation. Using a battery of assays to study leukocyte subsets and the morphology of lymphoid organs, as well as the humoral immune responses, we were unable to detect any difference between wild-type mice and the Y223F mutant. Mice resistant to irreversible BTK inhibitors, through a cysteine 481 to serine substitution (C481S), served as an additional immunization control and mounted similar humoral immune responses as Y223F and wild-type animals. Collectively, our findings suggest that phosphorylation of Y223 serves as a useful proxy for phosphorylation of phospholipase Cγ2 (PLCG2), the endogenous substrate of BTK. However, in contrast to a frequently held conception, this posttranslational modification is dispensable for the function of BTK.

Digestive and Absorptive Properties of the Antarctic Krill Tripeptide Phe-Pro-Phe (FPF) and Its Auxiliary Memory-Enhancing Effect

Aging and stress have contributed to the development of memory disorders. Phe-Pro-Phe (FPF) was identified with high stability by mass spectrometry from simulated gastrointestinal digestion and everted gut sac products of the Antarctic krill peptide Ser-Ser-Asp-Ala-Phe-Phe-Pro-Phe-Arg (SSDAFFPFR) which was found to have a positive impact on memory enhancement. This study investigated the digestive stability, absorption, and memory-enhancing effects of FPF using nuclear magnetic resonance spectroscopy, simulated gastrointestinal digestion, in vivo fluorescence distribution analysis, mouse behavioral experiments, acetylcholine function, Nissl staining, immunofluorescence, and immunohistochemistry. FPF crossed the blood-brain barrier into the brain after digestion, significantly reduced shock time, working memory errors, and reference memory errors, and increased the recognition index. Additionally, FPF elevated ACh content; Nissl body counts; and CREB, SYN, and PSD-95 expression levels, while reducing AChE activity (P < 0.05). This implies that FPF prevents scopolamine-induced memory impairment and provides a basis for future research on memory disorders.

GATA transcription factor in common bean: A comprehensive genome-wide functional characterization, identification, and abiotic stress response evaluation

The GATA transcription factors (TFs) have been extensively studied for its regulatory role in various biological processes in many plant species. The functional and molecular mechanism of GATA TFs in regulating tolerance to abiotic stress has not yet been studied in the common bean. This study analyzed the functional identity of the GATA gene family in the P. vulgaris genome under different abiotic and phytohormonal stress. The GATA gene family was systematically investigated in the P. vulgaris genome, and 31 PvGATA TFs were identified. The study found that 18 out of 31 PvGATA genes had undergone duplication events, emphasizing the role of gene duplication in GATA gene expansion. All the PvGATA genes were classified into four significant subfamilies, with 8, 3, 6, and 13 members in each subfamily (subfamilies I, II, III, and IV), respectively. All PvGATA protein sequences contained a single GATA domain, but subfamily II members had additional domains such as CCT and tify. A total of 799 promoter cis-regulatory elements (CREs) were predicted in the PvGATAs. Additionally, we used qRT-PCR to investigate the expression profiles of five PvGATA genes in the common bean roots under abiotic conditions. The results suggest that PvGATA01/10/25/28 may play crucial roles in regulating plant resistance against salt and drought stress and may be involved in phytohormone-mediated stress signaling pathways. PvGATA28 was selected for overexpression and cloned into N. benthamiana using Agrobacterium-mediated transformation. Transgenic lines were subjected to abiotic stress, and results showed a significant tolerance of transgenic lines to stress conditions compared to wild-type counterparts. The seed germination assay suggested an extended dormancy of transgenic lines compared to wild-type lines. This study provides a comprehensive analysis of the PvGATA gene family, which can serve as a foundation for future research on the function of GATA TFs in abiotic stress tolerance in common bean plants.

In silico design of misfolding resistant proteins: the role of structural similarity of a competing conformational ensemble in the optimization of frustration

Most state-of-the-art in silico design methods fail due to misfolding of designed sequences to a conformation other than the target. Thus, a method to design misfolding resistant proteins will provide a better understanding of the misfolding phenomenon and will also increase the success rate of in silico design methods. In this work, we optimize the conformational ensemble to be selected for negative design purposes based on the similarity of the conformational ensemble to the target. Five ensembles with different degrees of similarity to the target are created and destabilized and the target is stabilized while designing sequences using mean field theory and Monte Carlo simulation methods. The results suggest that the degree of similarity of the non-native conformations to the target plays a prominent role in designing misfolding resistant protein sequences. The design procedures that destabilize the conformational ensemble with moderate similarity to the target have proven to be more promising. Incorporation of either highly similar or highly dissimilar conformations to the target conformation into the non-native ensemble to be destabilized may lead to sequences with a higher misfolding propensity. This will significantly reduce the conformational space to be considered in any protein design procedure. Interestingly, the results suggest that a sequence with higher frustration in the target structure does not necessarily lead to a misfold prone sequence. A successful design method may purposefully choose a frustrated sequence in the target conformation if that sequence is even more frustrated in the competing non-native conformations.

Exploration of digestion-resistant immunodominant epitopes in shrimp (Penaeus vannamei) allergens

The digestion products of Penaeus vannamei still had sensitizing and eliciting capacity; however, the underlying mechanism has not been identified. This study analyzed the structural changes of shrimp proteins during digestion, predicted the linearmimotopepeptides and first validated the allergenicity of immunodominantepitopes with binding ability. The results showed that the shrimp proteins were gradually degraded into small peptides during digestion, which might lead to the destruction of linear epitopes. However, these peptides carried IgE epitopes that still trigger allergic reactions. Eighteen digestion-resistant epitopes were predicted by multiple immunoinformatics tools and digestomics. Five epitopes contained more critical amino acids and had strong molecular docking (P1: DSGVGIYAPDAEA, P2: EGELKGTYYPLTGM, P3: GRQGDPHGKFDLPPGV, P4: IFAWPHKDNNGIE, P5: KSTESSVTVPDVPSIHD), and these epitopes were identified as novel IgE binding immunodominantepitopes in Penaeus vannamei. These findings provide novel insight into allergenic epitopes, which might serve as key targets for reducing the allergenicity in shrimp.

Protein feature engineering framework for AMPylation site prediction

AMPylation is a biologically significant yet understudied post-translational modification where an adenosine monophosphate (AMP) group is added to Tyrosine and Threonine residues primarily. While recent work has illuminated the prevalence and functional impacts of AMPylation, experimental identification of AMPylation sites remains challenging. Computational prediction techniques provide a faster alternative approach. The predictive performance of machine learning models is highly dependent on the features used to represent the raw amino acid sequences. In this work, we introduce a novel feature extraction pipeline to encode the key properties relevant to AMPylation site prediction. We utilize a recently published dataset of curated AMPylation sites to develop our feature generation framework. We demonstrate the utility of our extracted features by training various machine learning classifiers, on various numerical representations of the raw sequences extracted with the help of our framework. Tenfold cross-validation is used to evaluate the model's capability to distinguish between AMPylated and non-AMPylated sites. The top-performing set of features extracted achieved MCC score of 0.58, Accuracy of 0.8, AUC-ROC of 0.85 and F1 score of 0.73. Further, we elucidate the behaviour of the model on the set of features consisting of monogram and bigram counts for various representations using SHapley Additive exPlanations.

Multiquery Similarity Searching Models: An Alternative Approach for Predicting Hemolytic Activity from Peptide Sequence

The desirable pharmacological properties and a broad number of therapeutic activities have made peptides promising drugs over small organic molecules and antibody drugs. Nevertheless, toxic effects, such as hemolysis, have hampered the development of such promising drugs. Hence, a reliable computational tool to predict peptide hemolytic toxicity is enormously useful before synthesis and experimental evaluation. Currently, four web servers that predict hemolytic activity using machine learning (ML) algorithms are available; however, they exhibit some limitations, such as the need for a reliable negative set and limited application domain. Hence, we developed a robust model based on a novel theoretical approach that combines network science and a multiquery similarity searching (MQSS) method. A total of 1152 initial models were constructed from 144 scaffolds generated in a previous report. These were evaluated on external data sets, and the best models were fused and improved. Our best MQSS model I1 outperformed all state-of-the-art ML-based models and was used to characterize the prevalence of hemolytic toxicity on therapeutic peptides. Based on our model's estimation, the number of hemolytic peptides might be 3.9-fold higher than the reported.

Genomic characterization and related functional genes of γ- poly glutamic acid producing Bacillus subtilis

γ- poly glutamic acid (γ-PGA), a high molecular weight polymer, is synthesized by microorganisms and secreted into the extracellular space. Due to its excellent performance, γ-PGA has been widely used in various fields, including food, biomedical and environmental fields. In this study, we screened natto samples for two strains of Bacillus subtilis N3378-2at and N3378-3At that produce γ-PGA. We then identified the γ-PGA synthetase gene cluster (PgsB, PgsC, PgsA, YwtC and PgdS), glutamate racemase RacE, phage-derived γ-PGA hydrolase (PghB and PghC) and exo-γ-glutamyl peptidase (GGT) from the genome of these strains. Based on these γ-PGA-related protein sequences from isolated Bacillus subtilis and 181 B. subtilis obtained from GenBank, we carried out genotyping analysis and classified them into types 1-5. Since we found B. amyloliquefaciens LL3 can produce γ-PGA, we obtained the B. velezensis and B. amyloliquefaciens strains from GenBank and classified them into types 6 and 7 based on LL3. Finally, we constructed evolutionary trees for these protein sequences. This study analyzed the distribution of γ-PGA-related protein sequences in the genomes of B. subtilis, B. velezensis and B. amyloliquefaciens strains, then the evolutionary diversity of these protein sequences was analyzed, which provided novel information for the development and utilization of γ-PGA-producing strains.

Structural insights into the N-terminal APHB domain of HrpA: mediating canonical and i-motif recognition

RNA helicases function as versatile enzymes primarily responsible for remodeling RNA secondary structures and organizing ribonucleoprotein complexes. In our study, we conducted a systematic analysis of the helicase-related activities of Escherichia coli HrpA and presented the structures of both its apo form and its complex bound with both conventional and non-canonical DNAs. Our findings reveal that HrpA exhibits NTP hydrolysis activity and binds to ssDNA and ssRNA in distinct sequence-dependent manners. While the helicase core plays an essential role in unwinding RNA/RNA and RNA/DNA duplexes, the N-terminal extension in HrpA, consisting of three helices referred to as the APHB domain, is crucial for ssDNA binding and RNA/DNA duplex unwinding. Importantly, the APHB domain is implicated in binding to non-canonical DNA structures such as G-quadruplex and i-motif, and this report presents the first solved i-motif-helicase complex. This research not only provides comprehensive insights into the multifaceted roles of HrpA as an RNA helicase but also establishes a foundation for further investigations into the recognition and functional implications of i-motif DNA structures in various biological processes.

KEGG orthology prediction of bacterial proteins using natural language processing

BACKGROUND

The advent of high-throughput technologies has led to an exponential increase in uncharacterized bacterial protein sequences, surpassing the capacity of manual curation. A large number of bacterial protein sequences remain unannotated by Kyoto Encyclopedia of Genes and Genomes (KEGG) orthology, making it necessary to use auto annotation tools. These tools are now indispensable in the biological research landscape, bridging the gap between the vastness of unannotated sequences and meaningful biological insights.

RESULTS

In this work, we propose a novel pipeline for KEGG orthology annotation of bacterial protein sequences that uses natural language processing and deep learning. To assess the effectiveness of our pipeline, we conducted evaluations using the genomes of two randomly selected species from the KEGG database. In our evaluation, we obtain competitive results on precision, recall, and F1 score, with values of 0.948, 0.947, and 0.947, respectively.

CONCLUSIONS

Our experimental results suggest that our pipeline demonstrates performance comparable to traditional methods and excels in identifying distant relatives with low sequence identity. This demonstrates the potential of our pipeline to significantly improve the accuracy and comprehensiveness of KEGG orthology annotation, thereby advancing our understanding of functional relationships within biological systems.

Quantifying negative selection on synonymous variants

Widespread adoption of DNA sequencing has resulted in large numbers of genetic variants, whose contribution to disease is not easily determined. Although many types of variation are known to disrupt cellular processes in predictable ways, for some categories of variants, the effects may not be directly detectable. A particular example is synonymous variants, that is, those single-nucleotide variants that create a codon substitution, such that the produced amino acid sequence is unaffected. Contrary to the original theory suggesting that synonymous variants are benign, there is a growing volume of research showing that, despite their "silent" mechanism of action, some synonymous variation may be deleterious. Here, we studied the extent of the negative selective pressure acting on different classes of synonymous variants by analyzing the relative enrichment of synonymous singleton variants in the human exomes provided by gnomAD. Using a modification of the mutability-adjusted proportion of singletons (MAPS) metric as a measure of purifying selection, we found that some classes of synonymous variants are subject to stronger negative selection than others. For instance, variants that reduce codon optimality undergo stronger selection than optimality-increasing variants. Besides, selection affects synonymous variants implicated in splice-site-loss or splice-site-gain events. To understand what drives this negative selection, we tested a number of predictors in the aim to explain the variability in the selection scores. Our findings provide insights into the effects of synonymous variants at the population level, highlighting the specifics of the role that these variants play in health and disease.

Molecular cloning, characterisation and molecular modelling of two novel T-synthases from mollusc origin

The glycoprotein-N-acetylgalactosamine β1,3-galactosyltransferase, known as T-synthase (EC 2.4.1.122), plays a crucial role in the synthesis of the T-antigen, which is the core 1 O-glycan structure. This enzyme transfers galactose from UDP-Gal to GalNAc-Ser/Thr. The T-antigen has significant functions in animal development, immune response, and recognition processes. Molluscs are a successful group of animals that inhabit various environments, such as freshwater, marine, and terrestrial habitats. They serve important roles in ecosystems as filter feeders and decomposers but can also be pests in agriculture and intermediate hosts for human and cattle parasites. The identification and characterization of novel carbohydrate active enzymes, such as T-synthase, can aid in the understanding of molluscan glycosylation abilities and their adaptation and survival abilities. Here, the T-synthase enzymes from the snail Pomacea canaliculata and the oyster Crassostrea gigas are identified, cloned, expressed, and characterized, with a focus on structural elucidation. The synthesized enzymes display core 1 β1,3-galactosyltransferase activity using pNP-α-GalNAc as substrate and exhibit similar biochemical parameters as previously characterised T-synthases from other species. While the enzyme from C. gigas shares the same structural parameters with the other enzymes characterised so far, the T-synthase from P. canaliculata lacks the consensus sequence CCSD, which was previously considered indispensable.

Kinetic Ensemble of Tau Protein through the Markov State Model and Deep Learning Analysis

The ordered assembly of Tau protein into filaments characterizes Alzheimer's and other neurodegenerative diseases, and thus, stabilization of Tau protein is a promising avenue for tauopathies therapy. To dissect the underlying aggregation mechanisms on Tau, we employ a set of molecular simulations and the Markov state model to determine the kinetics of ensemble of K18. K18 is the microtubule-binding domain of Tau protein and plays a vital role in the microtubule assembly, recycling processes, and amyloid fibril formation. Here, we efficiently explore the conformation of K18 with about 150 μs lifetimes in silico. Our results observe that all four repeat regions (R1-R4) are very dynamic, featuring frequent conformational conversion and lacking stable conformations, and the R2 region is more flexible than the R1, R3, and R4 regions. Additionally, it is worth noting that residues 300-310 in R2-R3 and residues 319-336 in R3 tend to form sheet structures, indicating that K18 has a broader functional role than individual repeat monomers. Finally, the simulations combined with Markov state models and deep learning reveal 5 key conformational states along the transition pathway and provide the information on the microsecond time scale interstate transition rates. Overall, this study offers significant insights into the molecular mechanism of Tau pathological aggregation and develops novel strategies for both securing tauopathies and advancing drug discovery.

Maize actin depolymerizing factor 1 (ZmADF1) negatively regulates pollen development

The normal development of pollen grains and the completion of double fertilization in embryos are crucial for both the sexual reproduction of angiosperms and grain production. Actin depolymerizing factor (ADF) regulates growth, development, and responses to biotic and abiotic stress by binding to actin in plants. In this study, the function of the ZmADF1 gene was validated through bioinformatic analysis, subcellular localization, overexpression in maize and Arabidopsis, and knockout via CRISPR/Cas9. The amino acid sequence of ZmADF1 exhibited high conservation and a similar tertiary structure to that of ADF homologs. Subcellular localization analysis revealed that ZmADF1 is localized mainly to the nucleus and cytoplasm. The ZmADF1 gene was specifically expressed in maize pollen, and overexpression of the ZmADF1 gene decreased the number of pollen grains in the anthers of transgenic Arabidopsis plants. The germination rate of pollen and the empty seed shell rate in the fruit pods of the overexpressing plants were significantly greater than those in the wild-type (WT) plants. In maize, the pollen viability of the knockout lines was significantly greater than that of both the WT and the overexpressing lines. Our results confirmed that the ZmADF1 gene was specifically expressed in pollen and negatively regulated pollen quantity, vigor, germination rate, and seed setting rate. This study provides insights into ADF gene function and possible pathways for improving high-yield maize breeding.

Single-cell characterisation of tissue homing CD4 + and CD8 + T cell clones in immune-mediated refractory arthritis

BACKGROUND

Immune-mediated arthritis is a group of autoinflammatory diseases, where the patient's own immune system attacks and destroys synovial joints. Sustained remission is not always achieved with available immunosuppressive treatments, warranting more detailed studies of T cell responses that perpetuate synovial inflammation in treatment-refractory patients.

METHODS

In this study, we investigated CD4 + and CD8 + T lymphocytes from the synovial tissue and peripheral blood of patients with treatment-resistant immune-mediated arthritis using paired single-cell RNA and TCR-sequencing. To gain insights into the trafficking of clonal families, we compared the phenotypes of clones with the exact same TCRß amino acid sequence between the two tissues.

RESULTS

Our results show that both CD4 + and CD8 + T cells display a more activated and inflamed phenotype in the synovial tissue compared to peripheral blood both at the population level and within individual T cell families. Furthermore, we found that both cell subtypes exhibited clonal expansion in the synovial tissue.

CONCLUSIONS

Our findings suggest that the local environment in the synovium drives the proliferation of activated cytotoxic T cells, and both CD4 + and CD8 + T cells may contribute to tissue destruction and disease pathogenesis.

Machine Learning Integrating Protein Structure, Sequence, and Dynamics to Predict the Enzyme Activity of Bovine Enterokinase Variants

Despite recent advances in computational protein science, the dynamic behavior of proteins, which directly governs their biological activity, cannot be gleaned from sequence information alone. To overcome this challenge, we propose a framework that integrates the peptide sequence, protein structure, and protein dynamics descriptors into machine learning algorithms to enhance their predictive capabilities and achieve improved prediction of the protein variant function. The resulting machine learning pipeline integrates traditional sequence and structure information with molecular dynamics simulation data to predict the effects of multiple point mutations on the fold improvement of the activity of bovine enterokinase variants. This study highlights how the combination of structural and dynamic data can provide predictive insights into protein functionality and address protein engineering challenges in industrial contexts.

Accurately clustering biological sequences in linear time by relatedness sorting

Clustering biological sequences into similar groups is an increasingly important task as the number of available sequences continues to grow exponentially. Search-based approaches to clustering scale super-linearly with the number of input sequences, making it impractical to cluster very large sets of sequences. Approaches to clustering sequences in linear time currently lack the accuracy of super-linear approaches. Here, I set out to develop and characterize a strategy for clustering with linear time complexity that retains the accuracy of less scalable approaches. The resulting algorithm, named Clusterize, sorts sequences by relatedness to linearize the clustering problem. Clusterize produces clusters with accuracy rivaling popular programs (CD-HIT, MMseqs2, and UCLUST) but exhibits linear asymptotic scalability. Clusterize generates higher accuracy and oftentimes much larger clusters than Linclust, a fast linear time clustering algorithm. I demonstrate the utility of Clusterize by accurately solving different clustering problems involving millions of nucleotide or protein sequences.

Accurate and Fast Prediction of Intrinsically Disordered Protein by Multiple Protein Language Models and Ensemble Learning

Intrinsically disordered proteins (IDPs) play a vital role in various biological processes and have attracted increasing attention in the past few decades. Predicting IDPs from the primary structures of proteins offers a rapid and facile means of protein analysis without necessitating crystal structures. In particular, machine learning methods have demonstrated their potential in this field. Recently, protein language models (PLMs) are emerging as a promising approach to extracting essential information from protein sequences and have been employed in protein modeling to utilize their advantages of precision and efficiency. In this article, we developed a novel IDP prediction method named IDP-ELM to predict the intrinsically disordered regions (IDRs) as well as their functions including disordered flexible linkers and disordered protein binding. This method utilizes high-dimensional representations extracted from several state-of-the-art PLMs and predicts IDRs by ensemble learning based on bidirectional recurrent neural networks. The performance of the method was evaluated on two independent test data sets from CAID (critical assessment of protein intrinsic disorder prediction) and CAID2, indicating notable improvements in terms of area under the receiver operating characteristic (AUC), Matthew's correlation coefficient (MCC), and F1 score. Moreover, IDP-ELM requires solely protein sequences as inputs and does not entail a time-consuming process of protein profile generation, which is a prerequisite for most existing state-of-the-art methods, enabling an accurate, fast, and convenient tool for proteome-level analysis. The corresponding reproducible source code and model weights are available at https://github.com/xu-shi-jie/idp-elm.

Enhancing missense variant pathogenicity prediction with protein language models using VariPred

Computational approaches for predicting the pathogenicity of genetic variants have advanced in recent years. These methods enable researchers to determine the possible clinical impact of rare and novel variants. Historically these prediction methods used hand-crafted features based on structural, evolutionary, or physiochemical properties of the variant. In this study we propose a novel framework that leverages the power of pre-trained protein language models to predict variant pathogenicity. We show that our approach VariPred (Variant impact Predictor) outperforms current state-of-the-art methods by using an end-to-end model that only requires the protein sequence as input. Using one of the best-performing protein language models (ESM-1b), we establish a robust classifier that requires no calculation of structural features or multiple sequence alignments. We compare the performance of VariPred with other representative models including 3Cnet, Polyphen-2, REVEL, MetaLR, FATHMM and ESM variant. VariPred performs as well as, or in most cases better than these other predictors using six variant impact prediction benchmarks despite requiring only sequence data and no pre-processing of the data.

DPI_CDF: druggable protein identifier using cascade deep forest

BACKGROUND

Drug targets in living beings perform pivotal roles in the discovery of potential drugs. Conventional wet-lab characterization of drug targets is although accurate but generally expensive, slow, and resource intensive. Therefore, computational methods are highly desirable as an alternative to expedite the large-scale identification of druggable proteins (DPs); however, the existing in silico predictor's performance is still not satisfactory.

METHODS

In this study, we developed a novel deep learning-based model DPI_CDF for predicting DPs based on protein sequence only. DPI_CDF utilizes evolutionary-based (i.e., histograms of oriented gradients for position-specific scoring matrix), physiochemical-based (i.e., component protein sequence representation), and compositional-based (i.e., normalized qualitative characteristic) properties of protein sequence to generate features. Then a hierarchical deep forest model fuses these three encoding schemes to build the proposed model DPI_CDF.

RESULTS

The empirical outcomes on 10-fold cross-validation demonstrate that the proposed model achieved 99.13 % accuracy and 0.982 of Matthew's-correlation-coefficient (MCC) on the training dataset. The generalization power of the trained model is further examined on an independent dataset and achieved 95.01% of maximum accuracy and 0.900 MCC. When compared to current state-of-the-art methods, DPI_CDF improves in terms of accuracy by 4.27% and 4.31% on training and testing datasets, respectively. We believe, DPI_CDF will support the research community to identify druggable proteins and escalate the drug discovery process.

AVAILABILITY

The benchmark datasets and source codes are available in GitHub: http://github.com/Muhammad-Arif-NUST/DPI_CDF .

Insights from structure-activity relationships and the binding mode of peptidic α-ketoamide inhibitors of the malaria drug target subtilisin-like SUB1

Plasmodium multi-resistance, including against artemisinin, seriously threatens malaria treatment and control. Hence, new drugs are urgently needed, ideally targeting different parasitic stages, which are not yet targeted by current drugs. The SUB1 protease is involved in both hepatic and blood stages due to its essential role in the egress of parasites from host cells, and, as potential new target, it would meet the above criteria. We report here the synthesis as well as the biological and structural evaluation of substrate-based α-ketoamide SUB1 pseudopeptidic inhibitors encompassing positions P4-P2'. By individually substituting each position of the reference compound 1 (MAM-117, Ac-Ile-Thr-Ala-AlaCO-Asp-Glu (Oall)-NH2), we better characterized the structural determinants for SUB1 binding. We first identified compound 8 with IC50 values of 50 and 570 nM against Pv- and PfSUB1, respectively (about 3.5-fold higher potency compared to 1). Compound 8 inhibited P. falciparum merozoite egress in culture by 37% at 100 μM. By increasing the overall hydrophobicity of the compounds, we could improve the PfSUB1 inhibition level and antiparasitic activity, as shown with compound 40 (IC50 values of 12 and 10 nM against Pv- and PfSUB1, respectively, IC50 value of 23 μM on P. falciparum merozoite egress). We also found that 8 was highly selective towards SUB1 over three mammalian serine peptidases, supporting the promising value of this compound. Finally, several crystal 3D-structures of SUB1-inhibitor complexes, including with 8, were solved at high resolution to decipher the binding mode of these compounds.

EncoMPASS: An encyclopedia of membrane proteins analyzed by structure and symmetry

Protein structure determination and prediction, active site detection, and protein sequence alignment techniques all exploit information about protein structure and structural relationships. For membrane proteins, however, there is limited agreement among available online tools for highlighting and mapping such structural similarities. Moreover, no available resource provides a systematic overview of quaternary and internal symmetries, and their orientation relative to the membrane, despite the fact that these properties can provide key insights into membrane protein function and evolution. Here, we describe the Encyclopedia of Membrane Proteins Analyzed by Structure and Symmetry (EncoMPASS), a database for relating integral membrane proteins of known structure from the points of view of sequence, structure, and symmetry. EncoMPASS is accessible through a web interface, and its contents can be easily downloaded. This allows the user not only to focus on specific proteins, but also to study general properties of the structure and evolution of membrane proteins.

Characterization, Semirational Design for pH Robustness, and the Application in Bioactive Peptide Production of a X-Prolyl Dipeptidyl Aminopeptidase from Lactococcus lactis MY-3

PepXLcMY-3, an X-prolyl dipeptidyl aminopeptidase derived from Lactobacillus lactis MY-3, was screened and recombinantly expressed in Escherichia coli. The enzyme could exhibit about 40% activity within the pH range of 6.0-10. To further improve the pH robustness, site E396 located in the active pocket was discovered through alanine scanning. The mutant E396I displayed both developed activity and kcat/Km. The optimal pH of E396I shifted from 6.0 to 10 compared to WT, with the relative activity within the pH range of 6.0-10 significantly increased. The site K648 was then proposed by semirational design. The activity of mutant E396I/K648D reached 4.03 U/mg. The optimal pH was restored to 6.0, and the pH stability was further improved. E396I/K648D could totally hydrolyze β-casomorphin 7 within 30 min. The hydrolysate showed 64.5% inhibition on angiotensin I converting enzyme, which was more efficient than those produced by E396I and WT, 23.2 and 44.7%, respectively.

CAT-DTI: cross-attention and Transformer network with domain adaptation for drug-target interaction prediction

Accurate and efficient prediction of drug-target interaction (DTI) is critical to advance drug development and reduce the cost of drug discovery. Recently, the employment of deep learning methods has enhanced DTI prediction precision and efficacy, but it still encounters several challenges. The first challenge lies in the efficient learning of drug and protein feature representations alongside their interaction features to enhance DTI prediction. Another important challenge is to improve the generalization capability of the DTI model within real-world scenarios. To address these challenges, we propose CAT-DTI, a model based on cross-attention and Transformer, possessing domain adaptation capability. CAT-DTI effectively captures the drug-target interactions while adapting to out-of-distribution data. Specifically, we use a convolution neural network combined with a Transformer to encode the distance relationship between amino acids within protein sequences and employ a cross-attention module to capture the drug-target interaction features. Generalization to new DTI prediction scenarios is achieved by leveraging a conditional domain adversarial network, aligning DTI representations under diverse distributions. Experimental results within in-domain and cross-domain scenarios demonstrate that CAT-DTI model overall improves DTI prediction performance compared with previous methods.

A quest for cytosolic sequons and their functions

Evolution shapes protein sequences for their functions. Here, we studied the moonlighting functions of the N-linked sequon NXS/T, where X is not P, in human nucleocytosolic proteins. By comparing membrane and secreted proteins in which sequons are well known for N-glycosylation, we discovered that cyto-sequons can participate in nucleic acid binding, particularly in zinc finger proteins. Our global studies further discovered that sequon occurrence is largely proportional to protein length. The contribution of sequons to protein functions, including both N-glycosylation and nucleic acid binding, can be regulated through their density as well as the biased usage between NXS and NXT. In proteins where other PTMs or structural features are rich, such as phosphorylation, transmembrane ɑ-helices, and disulfide bridges, sequon occurrence is scarce. The information acquired here should help understand the relationship between protein sequence and function and assist future protein design and engineering.

Prediction of interactions between cell surface proteins by machine learning

Cells detect changes in their external environments or communicate with each other through proteins on their surfaces. These cell surface proteins form a complicated network of interactions in order to fulfill their functions. The interactions between cell surface proteins are highly dynamic and, thus, challenging to detect using traditional experimental techniques. Here, we tackle this challenge using a computational framework. The primary focus of the framework is to develop new tools to identify interactions between domains in the immunoglobulin (Ig) fold, which is the most abundant domain family in cell surface proteins. These interactions could be formed between ligands and receptors from different cells or between proteins on the same cell surface. In practice, we collected all structural data on Ig domain interactions and transformed them into an interface fragment pair library. A high-dimensional profile can then be constructed from the library for a given pair of query protein sequences. Multiple machine learning models were used to read this profile so that the probability of interaction between the query proteins could be predicted. We tested our models on an experimentally derived dataset that contains 564 cell surface proteins in humans. The cross-validation results show that we can achieve higher than 70% accuracy in identifying the PPIs within this dataset. We then applied this method to a group of 46 cell surface proteins in Caenorhabditis elegans. We screened every possible interaction between these proteins. Many interactions recognized by our machine learning classifiers have been experimentally confirmed in the literature. In conclusion, our computational platform serves as a useful tool to help identify potential new interactions between cell surface proteins in addition to current state-of-the-art experimental techniques. The tool is freely accessible for use by the scientific community. Moreover, the general framework of the machine learning classification can also be extended to study the interactions of proteins in other domain superfamilies.

Defining Biological and Biochemical Functions of Noncanonical SET Domain Proteins

Within the SET domain superfamily of lysine methyltransferases, there is a well-conserved subfamily, frequently referred to as the Set3 SET domain subfamily, which contain noncanonical SET domains carrying divergent amino acid sequences. These proteins are implicated in diverse biological processes including stress responses, cell differentiation, and development, and their disruption is linked to diseases including cancer and neurodevelopmental disorders. Interestingly, biochemical and structural analysis indicates that they do not possess catalytic methyltransferase activity. At the molecular level, Set3 SET domain proteins appear to play critical roles in the regulation of gene expression, particularly repression and heterochromatin maintenance, and in some cases, via scaffolding other histone modifying activities at chromatin. Here, we explore the common and unique functions among Set3 SET domain subfamily proteins and analyze what is known about the specific contribution of the conserved SET domain to functional roles of these proteins, as well as propose areas of investigation to improve understanding of this important, noncanonical subfamily of proteins.

Molecular and structural analysis of Hdh-MIRP3 and its impact on reproductive regulation in female Pacific abalone, Haliotis discus hannai

Molluscan insulin-related peptides (MIRP) play a crucial role in various biological processes, including reproduction and larval development in mollusk species. To investigate the involvement of MIRP in the ovarian development of Pacific abalone (Haliotis discus hannai), the Hdh-MIRP3 was cloned from cerebral ganglion (CG). Hdh-MIRP3 cDNA was 993 bp long, encoded a 13.22 kDa peptide, comprising 118 amino acids. Fluorescence in situ hybridization confirmed the localization of Hdh-MIRP3 in the CG and ovary. Molecular docking revealed that Hdh-MIRP3 binds to the N-terminal region of Hdh-IRP-R. Tissue expression analysis showed the highest Hdh-MIRP3 expression in the CG, followed by ovarian tissue. Hdh-MIRP3 expression was significantly upregulated in the CG and ovary during the ripe stage of seasonal ovarian development and in effective accumulative temperature conditioned abalone. Furthermore, siRNA silencing of Hdh-MIRP3 significantly downregulated the expression of four reproduction-related genes, including Hdh-GnRH, Hdh-GnRH-R, Hdh-IRP-R, and Hdh-VTG in both the CG and ovary, and Hdh-MIRP3 as well. These results indicate that Hdh-MIRP3 acts as a regulator of ovarian development in Pacific abalone. Additionally, expression analysis indicated that Hdh-MIRP3 plays a role in embryonic and larval development. Overall, the present findings elucidate the role of Hdh-MIRP3 in reproductive development in female Pacific abalone.

Slc43a2+ T cell metastasis from spleen to brain in RGNNV infected teleost

The origin of T cells in the teleost's brain is unclear. While viewing the central nervous system (CNS) as immune privileged has been widely accepted, previous studies suggest that T cells residing in the thymus but not in the spleen of the teleost play an essential role in communicating with the peripheral organs. Here, we identified nine T cell subpopulations in the thymus and spleen of orange-spotted grouper (Epinephelus coioices) through single-cell RNA-sequencing analysis. After viral CNS infection with red-spotted grouper nervous necrosis virus (RGNNV), the number of slc43a2+ T cells synchronously increased in the spleen and brain. During the infection tests in asplenic zebrafish (tlx1▲ zebrafish model), no increase in the number of slc43a2+ T cells was observed in the brain. Single-cell transcriptomic analysis indicated that slc43a2+ T cells mature and functionally differentiate within the spleen and then migrate into the brain to trigger an immune response. This study suggests a novel route for T cell migration from the spleen to the brain during viral infection in fish.

Real-time detection of 20 amino acids and discrimination of pathologically relevant peptides with functionalized nanopore

Precise identification and quantification of amino acids is crucial for many biological applications. Here we report a copper(II)-functionalized Mycobacterium smegmatis porin A (MspA) nanopore with the N91H substitution, which enables direct identification of all 20 proteinogenic amino acids when combined with a machine-learning algorithm. The validation accuracy reaches 99.1%, with 30.9% signal recovery. The feasibility of ultrasensitive quantification of amino acids was also demonstrated at the nanomolar range. Furthermore, the capability of this system for real-time analyses of two representative post-translational modifications (PTMs), one unnatural amino acid and ten synthetic peptides using exopeptidases, including clinically relevant peptides associated with Alzheimer's disease and cancer neoantigens, was demonstrated. Notably, our strategy successfully distinguishes peptides with only one amino acid difference from the hydrolysate and provides the possibility to infer the peptide sequence.

Dual oxidase 2 (duox 2) participates in the intestinal antibacterial innate immune responses of Procambarus clarkii by regulating ROS levels

Dual oxidase (Duox) a member of the nicotinamide adenine dinucleotide phosphate oxidase (NOX) family can induce the production of reactive oxygen species (ROS). In vertebrates, the duox gene was indicated to be associated with the mucosal immunity. The roles of the duox gene in invertebrates were mainly studied in insects for the function of maintaining intestinal flora balance. In recent years, some studies have reported that Duox is involved in regulating the production of ROS and plays an important role in defending against the intestinal pathogen infection. However, the molecular mechanism has not been fully illuminated. In this study, a duox 2 involved in the production of H2O2 was identified for the first time in P. clarkii. Mature Pc-Duox 2 is a 7-transmembrane protein molecule that includes PHD, FAD, and NAD domains. Pc-duox 2 was mainly expressed in hemocytes and intestinal tissue. Its expression levels were obviously upregulated after intramuscular or oral infection with V. harveyi. In the RNAi assay, the upregulated trends of H2O2 and total ROS levels in crayfish intestine were significantly suppressed when Pc-duox 2 was knocked down. Compared with the slightly affected SOD activity, the upregulated CAT activity was suppressed more obviously in the crayfish intestine. Furthermore, Pc-duox 2 had an important effect on the maintenance of the structural stability of crayfish the intestine. Further research revealed that the knockdown of Pc-duox 2 could cause an obvious suppression in the upregulated levels of Toll signalling pathway-related genes, including Pc-toll 1, Pc-toll 3, Pc-dorsal, Pc-ALF 5, Pc-crustin 1, and Pc-lysozyme. Ultimately, these changes triggered the accelerated death of crayfish. Overall, we speculated that Pc-duox 2 played an important role in antibacterial innate immunity in the crayfish intestine by regulating the total ROS level.

Structural insights of Labeo catla (catla) myxovirus resistance protein,GTP binding recognition and constitutive expression induced with Poly I:C

The Myxovirus resistance (Mx) proteins are critical effectors belonging to the super-family of guanidine triphosphatase, often stimulated by type I interferon (IFN) and mediates antiviral responses to restrict the replication of numerous viral genes in fishes. In teleosts, Mx proteins display diverse and complicated antiviral activity in different species. The present investigation seeks to characterize the Mx gene from Labeo catla upon induction by double-stranded (ds) RNA, polyinosinic-polycytidylic acid, (poly I: C). Molecular modeling and all-atoms molecular dynamics (MD) simulations were employed to understand the architecture of the GTPase domain and its plausible mode of GTP recognition in Mx protein. The full-length L. catla Mx (LcMx) gene sequence (1821 bp nucleotides) encodes an open reading frame of 606 amino acids. Domain search indicated conserved tripartite domain architecture of LcMx and forms a major cluster with the Mx from other teleosts. The positively charged Arginine and polar Glutamine residues from helix 3 and 4 of stalk region LcMx aid in homo-oligomerization. MD simulation portrayed the role of conserved critical residues aid in GTP recognition by the GTPase domain which perfectly corroborates with experimental findings and prior MD studies. After injection of poly I:C, the temporal mRNA profile showed that LcMx expression was significantly elevated in the spleen, brain, kidney, liver, muscle, heart, intestine, and gill tissues. Collectively, these results suggest that the elevated expression of the major innate immune defense gene Mx was able to inhibit the poly I: C mediated virulence in fish.Communicated by Ramaswamy H. Sarma.

Deciphering the structure of a distinctive trimodular cellulosomal licheninase (RfGH16_21), a family 16 glycoside hydrolase from Ruminococcus flavefaciens by computational and experimental methods

In order to know the insights of a unique naturally existing trimodular licheninase from GH16 family, sub-family 21 (RfGH16_21) from Ruminococcus flavefaciens, its structure was modeled to understand its functional relations to reveal information regarding modifying the enzyme for improved properties with enhanced catalytic efficiency. Homology modeling revealed three tandem repeats of β-jelly roll like folds linked by natural linkers. Catalytic pockets and the catalytically important amino acids in each tandem repeat of RfGH16_21 determined by multiple sequence alignment and structure superposition with its homologues indicated that two Glu residues are involved in a retaining-type of catalytic mechanism. Sequential molecular docking revealed maximum binding energy with mixed linked cellotriose showing that cellotriose is the lowest oligomeric hydrolysed product formed by the catalytic action of endo-β-1,3-1,4-glucanase. Molecular dynamic (MD) simulation of RfGH16_21-cellotriose complex confirmed the structural specificity of catalytic residues and increased stability of enzyme in presence of ligand as compared to simulated RfGH16_21 alone. The binding affinity of cellotriose towards the three tandem repeats of RfGH16_21 was also confirmed by calculating total binding Gibbs free energy, i.e. -100.8 ± 2.6 KJ/mol, by using g_mmpbsa tool. The stability of the protein was determined by protein melting analysis that showed Ca2+ and Mg2+ ions imparted structural stability to RfGH16_21. Dynamic light scattering analysis of RfGH16_21 showed monodispersity and hydrodynamic radius of 4.0 nm at 2.0 mg/mL protein concentration, which was comparable with the radius of gyration of 3.2 nm determined by MD simulation showing the protein to be in monomeric form.Communicated by Ramaswamy H. Sarma.