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The 3-hydroxypropionate/4-hydroxybutyrate (3HP/4HB) cycle from ammonia-oxidizing Thaumarchaeota is currently considered the most energy-efficient aerobic carbon fixation pathway. The Nitrosopumilus maritimus 4-hydroxybutyryl-CoA synthetase (ADP-forming; Nmar_0206) represents one of several enzymes from this cycle that exhibit increased efficiency over crenarchaeal counterparts. This enzyme reduces energy requirements on the cell, reflecting thaumarchaeal success in adapting to low-nutrient environments. Here we show the structure of Nmar_0206 from Nitrosopumilus maritimus SCM1, which reveals a highly conserved interdomain linker loop between the CoA-binding and ATP-grasp domains. Phylogenetic analysis suggests the widespread prevalence of this loop and highlights both its underrepresentation within the PDB and structural importance within the (ATP-forming) acyl-CoA synthetase (ACD) superfamily. This linker is shown to have a possible influence on conserved interface interactions between domains, thereby influencing homodimer stability. These results provide a structural basis for the energy efficiency of this key enzyme in the modified 3HP/4HB cycle of Thaumarchaeota.

Vector-borne bacterial pathogens cause devastating plant diseases that cost billions of dollars in crop losses worldwide. These pathogens have evolved to be host- and vector-dependent, resulting in a reduced genome size compared to their free-living relatives. All known vector-borne bacterial plant pathogens belong to four different genera: Candidatus Liberibacter, Candidatus Phytoplasma, Spiroplasma and Xylella. To protect themselves against pathogens, plants have evolved pattern recognition receptors that can detect conserved pathogen features as non-self and mount an immune response. To gain an understanding of how vector-borne pathogen features are perceived in plants, we investigated three proteinaceous features derived from cold shock protein (csp22), flagellin (flg22) and elongation factor Tu (elf18) from vector-borne bacterial pathogens as well as their closest free-living relatives. In general, vector-borne pathogens have fewer copies of genes encoding flagellin and cold shock protein compared to their closest free-living relatives. Furthermore, epitopes from vector-borne pathogens were less likely to be immunogenic compared to their free-living counterparts. Most Liberibacter csp22 and elf18 epitopes do not trigger plant immune responses in tomato or Arabidopsis. Interestingly, csp22 from the citrus pathogen Candidatus Liberibacter asiaticus triggers immune responses in solanaceous plants, while csp22 from the solanaceous pathogen Candidatus Liberibacter solanacearum does not. Our findings suggest that vector-borne plant pathogenic bacteria evolved to evade host recognition.

The poxvirus-derived protein vCCI (viral CC chemokine inhibitor) binds almost all members of the CC chemokine family with nanomolar affinity, inhibiting their pro-inflammatory actions. Understanding the affinity and specificity of vCCI could lead to new anti-inflammatory therapeutics. CCL17, also known as TARC, is unusual among CC chemokines by having only micromolar binding to vCCI. We have used sequence analysis and molecular simulations to determine the cause of this weak binding, which identified several locations in CCL17 where mutations seemed likely to improve binding to vCCI. Based on the aforementioned analysis, we expressed and tested multiple mutants of CCL17. We found two single point mutants V44K and Q45R that increased binding affinity to vCCI by 2-3-fold and, in combination, further improved affinity by 7-fold. The CCL17 triple mutant G17R/V44K/Q45R yielded a Kd of 0.25 ± 0.13 μM, a 68-fold improvement in affinity compared to the complex with wild-type CCL17. A quadruple mutant G17R/V44K/Q45R/R57W showed high affinity (0.59 ± 0.09 μM) compared to the wild type but lower affinity than the triple mutant. This work demonstrates that sequence comparisons and molecular simulations can predict chemokine mutations that increase the level of binding to vCCI, an important first step in developing engineered chemokine inhibitors useful for anti-inflammatory therapy.

Protein translation initiation is a conserved process involving many proteins acting in concert. The 13 subunit eukaryotic initiation factor 3 (eIF3) complex is essential for assembly of the pre-initiation complex that scans mRNA and positions ribosome at the initiation codon. We previously reported that a gain-of-function (gf) mutation affecting the G subunit of the Caenorhabditis elegans eIF3 complex, eif-3.g(gf), selectively modulates protein translation in the ventral cord cholinergic motor neurons. Here, through unbiased genetic suppressor screening, we identified that the gene lin-66 mediates eif-3.g(gf)-dependent protein translation in motor neurons. LIN-66 is composed largely of low-complexity amino acid sequences with unknown functional domains. We combined bioinformatics analysis with in vivo functional dissection and identified a cold-shock domain in LIN-66 critical for its function. In cholinergic motor neurons, LIN-66 shows a close association with EIF-3.G in the cytoplasm. The low-complexity amino acid sequences of LIN-66 modulate its subcellular pattern. As cold-shock domains function broadly in RNA regulation, we propose that LIN-66 mediates stimulus-dependent protein translation by facilitating the interaction of mRNAs with EIF-3.G.

Computational protein design is advancing rapidly. Here we describe efficient routes starting from validated parallel and antiparallel peptide assemblies to design two families of α-helical barrel proteins with central channels that bind small molecules. Computational designs are seeded by the sequences and structures of defined de novo oligomeric barrel-forming peptides, and adjacent helices are connected by loop building. For targets with antiparallel helices, short loops are sufficient. However, targets with parallel helices require longer connectors; namely, an outer layer of helix-turn-helix-turn-helix motifs that are packed onto the barrels. Throughout these computational pipelines, residues that define open states of the barrels are maintained. This minimizes sequence sampling, accelerating the design process. For each of six targets, just two to six synthetic genes are made for expression in Escherichia coli. On average, 70% of these genes express to give soluble monomeric proteins that are fully characterized, including high-resolution structures for most targets that match the design models with high accuracy.

Antigenically sequence variable M proteins of the major bacterial pathogen Streptococcus pyogenes (Strep A) are responsible for recruiting human C4b-binding protein (C4BP) to the bacterial surface, which enables Strep A to evade destruction by the immune system. The most sequence divergent portion of M proteins, the hypervariable region (HVR), is responsible for binding C4BP. Structural evidence points to the conservation of two C4BP-binding sequence patterns (M2 and M22) in the HVR of numerous M proteins, with this conservation applicable to vaccine immunogen design. These two patterns, however, only partially explain C4BP binding by Strep A. Here, we identified several M proteins that lack these patterns but still bind C4BP and determined the structures of two, M68 and M87 HVRs, in complex with a C4BP fragment. Mutagenesis of these M proteins led to the identification of amino acids that are crucial for C4BP binding, enabling formulation of new C4BP-binding patterns. Mutagenesis was also carried out on M2 and M22 proteins to refine or generate experimentally grounded C4BP-binding patterns. The M22 pattern was the most prevalent among M proteins, followed by the M87 and M2 patterns, while the M68 pattern was rare. These patterns, except for M68, were also evident in numerous M-like Enn proteins. Binding of C4BP via these patterns to Enn proteins was verified. We conclude that C4BP-binding patterns occur frequently in Strep A strains of differing M types, being present in their M or Enn proteins, or frequently both, providing further impetus for their use as vaccine immunogens.

Crystal structures of human long-chain acyl-CoA dehydrogenase (LCAD) and the catalytically inactive Glu291Gln mutant, have been determined. These structures suggest that LCAD harbors functions beyond its historically defined role in mitochondrial β-oxidation of long and medium-chain fatty acids. LCAD is a homotetramer containing one FAD per 43 kDa subunit with Glu291 as the catalytic base. The substrate binding cavity of LCAD reveals key differences which makes it specific for longer and branched chain substrates. The presence of Pro132 near the start of the E helix leads to helix unwinding that, together with adjacent smaller residues, permits binding of bulky substrates such as 3α, 7α, l2α-trihydroxy-5β-cholestan-26-oyl-CoA. This structural element is also utilized by ACAD11, a eucaryotic ACAD of unknown function, as well as bacterial ACADs known to metabolize sterol substrates. Sequence comparison suggests that ACAD10, another ACAD of unknown function, may also share this substrate specificity. These results suggest that LCAD, ACAD10, ACAD11 constitute a distinct class of eucaryotic acyl CoA dehydrogenases.

Transmembrane (TM) domains as simple as a single span can perform complex biological functions using entirely lipid-embedded chemical features. Computational design has the potential to generate custom tool molecules directly targeting membrane proteins at their functional TM regions. Thus far, designed TM domain-targeting agents have been limited to mimicking the binding modes and motifs of natural TM interaction partners. Here, we demonstrate the design of de novo TM proteins targeting the erythropoietin receptor (EpoR) TM domain in a custom binding topology competitive with receptor homodimerization. The TM proteins expressed in mammalian cells complex with EpoR and inhibit erythropoietin-induced cell proliferation. In vitro, the synthetic TM domain complex outcompetes EpoR homodimerization. Structural characterization reveals that the complex involves the intended amino acids and agrees with our designed molecular model of antiparallel TM helices at 1:1 stoichiometry. Thus, membrane protein TM regions can now be targeted in custom-designed topologies.

SLC22A10 is an orphan transporter with unknown substrates and function. The goal of this study is to elucidate its substrate specificity and functional characteristics. In contrast to orthologs from great apes, human SLC22A10, tagged with green fluorescent protein, is not expressed on the plasma membrane. Cells expressing great ape SLC22A10 orthologs exhibit significant accumulation of estradiol-17β-glucuronide, unlike those expressing human SLC22A10. Sequence alignments reveal a proline at position 220 in humans, which is a leucine in great apes. Replacing proline with leucine in SLC22A10-P220L restores plasma membrane localization and uptake function. Neanderthal and Denisovan genomes show proline at position 220, akin to modern humans, indicating functional loss during hominin evolution. Human SLC22A10 is a unitary pseudogene due to a fixed missense mutation, P220, while in great apes, its orthologs transport sex steroid conjugates. Characterizing SLC22A10 across species sheds light on its biological role, influencing organism development and steroid homeostasis.

Since the onset of the coronavirus disease 2019 (COVID-19) pandemic, SARS-CoV-2 variants capable of breakthrough infections have attracted global attention. These variants have significant mutations in the receptor-binding domain (RBD) of the spike protein and the membrane (M) protein, which may imply an enhanced ability to evade immune responses. In this study, an examination of co-mutations within the spike RBD and their potential correlation with mutations in the M protein was conducted. The EVmutation method was utilized to analyze the distribution of the mutations to elucidate the relationship between the mutations in the spike RBD and the alterations in the M protein. Additionally, the Sequence-to-Sequence Transformer Model (S2STM) was employed to establish mapping between the amino acid sequences of the spike RBD and M proteins, offering a novel and efficient approach for streamlined sequence analysis and the exploration of their interrelationship. Certain mutations in the spike RBD, G339D-S373P-S375F and Q493R-Q498R-Y505, are associated with a heightened propensity for inducing mutations at specific sites within the M protein, especially sites 3 and 19/63. These results shed light on the concept of mutational synergy between the spike RBD and M proteins, illuminating a potential mechanism that could be driving the evolution of SARS-CoV-2.

Vocal production learning (vocal learning) is a convergently evolved trait in vertebrates. To identify brain genomic elements associated with mammalian vocal learning, we integrated genomic, anatomical, and neurophysiological data from the Egyptian fruit bat (Rousettus aegyptiacus) with analyses of the genomes of 215 placental mammals. First, we identified a set of proteins evolving more slowly in vocal learners. Then, we discovered a vocal motor cortical region in the Egyptian fruit bat, an emergent vocal learner, and leveraged that knowledge to identify active cis-regulatory elements in the motor cortex of vocal learners. Machine learning methods applied to motor cortex open chromatin revealed 50 enhancers robustly associated with vocal learning whose activity tended to be lower in vocal learners. Our research implicates convergent losses of motor cortex regulatory elements in mammalian vocal learning evolution.

The design of protein-protein interfaces using physics-based design methods such as Rosetta requires substantial computational resources and manual refinement by expert structural biologists. Deep learning methods promise to simplify protein-protein interface design and enable its application to a wide variety of problems by researchers from various scientific disciplines. Here, we test the ability of a deep learning method for protein sequence design, ProteinMPNN, to design two-component tetrahedral protein nanomaterials and benchmark its performance against Rosetta. ProteinMPNN had a similar success rate to Rosetta, yielding 13 new experimentally confirmed assemblies, but required orders of magnitude less computation and no manual refinement. The interfaces designed by ProteinMPNN were substantially more polar than those designed by Rosetta, which facilitated in vitro assembly of the designed nanomaterials from independently purified components. Crystal structures of several of the assemblies confirmed the accuracy of the design method at high resolution. Our results showcase the potential of deep learning-based methods to unlock the widespread application of designed protein-protein interfaces and self-assembling protein nanomaterials in biotechnology.

The Novel Metagenome Protein Families Database (NMPFamsDB) is a database of metagenome- and metatranscriptome-derived protein families, whose members have no hits to proteins of reference genomes or Pfam domains. Each protein family is accompanied by multiple sequence alignments, Hidden Markov Models, taxonomic information, ecosystem and geolocation metadata, sequence and structure predictions, as well as 3D structure models predicted with AlphaFold2. In its current version, NMPFamsDB hosts over 100 000 protein families, each with at least 100 members. The reported protein families significantly expand (more than double) the number of known protein sequence clusters from reference genomes and reveal new insights into their habitat distribution, origins, functions and taxonomy. We expect NMPFamsDB to be a valuable resource for microbial proteome-wide analyses and for further discovery and characterization of novel functions. NMPFamsDB is publicly available in http://www.nmpfamsdb.org/ or https://bib.fleming.gr/NMPFamsDB.

Transcription factors (TFs) provide potentially powerful tools for plant metabolic engineering as they often control multiple genes in a metabolic pathway. However, selecting the best TF for a particular pathway has been challenging, and the selection often relies significantly on phylogenetic relationships. Here, we offer examples where evolutionary relationships have facilitated the selection of the suitable TFs, alongside situations where such relationships are misleading from the perspective of metabolic engineering. We argue that the evolutionary trajectory of a particular TF might be a better indicator than protein sequence homology alone in helping decide the best targets for plant metabolic engineering efforts. This article is part of the theme issue 'The evolution of plant metabolism'. [ABSTRACT FROM AUTHOR]

Babesia bovis, an apicomplexan intraerythrocytic protozoan parasite, causes serious economic loss to cattle industries around the world. Infection with this parasite leads to accumulation of infected red blood cells (iRBCs) in the brain microvasculature that results in severe clinical complications known as cerebral babesiosis. Throughout its growth within iRBCs, the parasite exports various proteins to the iRBCs that lead to the formation of protrusions known as "ridges" on the surface of iRBCs, which serve as sites for cytoadhesion to endothelial cells. Spherical body proteins (SBPs; proteins secreted from spherical bodies, which are organelles specific to Piroplasmida) are exported into iRBCs, and four proteins (SBP1-4) have been reported to date. In this study, we elucidated the function of SBP3 using an inducible gene knockdown (KD) system. Localization of SBP3 was assessed by immunofluorescence assay, and only partial colocalization was detected between SBP3 and SBP4 inside the iRBCs. In contrast, colocalization was observed with VESA-1, which is a major parasite ligand responsible for the cytoadhesion. Immunoelectron microscopy confirmed localization of SBP3 at the ridges. SBP3 KD was performed using the glmS system, and effective KD was confirmed by Western blotting, immunofluorescence assay, and RNA-seq analysis. The SBP3 KD parasites showed severe growth defect suggesting its importance for parasite survival in the iRBCs. VESA-1 on the surface of iRBCs was scarcely detected in SBP3 KD parasites, whereas SBP4 was still detected in the iRBCs. Moreover, abolition of ridges on the iRBCs and reduction of iRBCs cytoadhesion to the bovine brain endothelial cells were observed in SBP3 KD parasites. Immunoprecipitation followed by mass spectrometry analysis detected the host Band 3 multiprotein complex, suggesting an association of SBP3 with iRBC cytoskeleton proteins. Taken together, this study revealed the vital role of SBP3 in ridge formation and its significance in the pathogenesis of cerebral babesiosis. Author summary: Babesia bovis causes a high-mortality complication called cerebral babesiosis in cattle, similar to cerebral malaria in humans. Both complications are caused by the cytoadhesion of infected red blood cells (iRBCs) to the host brain endothelial cells. These parasites export numerous proteins to the host iRBCs and produce protrusions on the iRBCs that are called ridges for B. bovis and knobs for Plasmodium falciparum. Ridges and knobs play an important role in cytoadhesion as they are the sites of adherence; however, the molecules responsible for ridge formation remain unknown. In this study, we showed that SBP3 is a crucial protein for ridge formation. The SBP3 knockdown parasites showed severe growth defects and abolition of ridges on the iRBCs, and cytoadhesion of iRBCs to the bovine brain endothelial cells was significantly reduced. An immunoprecipitation experiment suggested an association of SBP3 with the host Band 3 multiprotein complex. Although there is no similarity in amino acid sequences, we suggest SBP3 to be a functional analog of KAHRP in P. falciparum. In summary, our results shed light on the molecular mechanism of ridge formation and the pathogenesis of B. bovis. [ABSTRACT FROM AUTHOR]

Mitochondria and plastids import thousands of proteins. Their experimental localisation remains a frequent task, but can be resource-intensive and sometimes impossible. Hence, hundreds of studies make use of algorithms that predict a localisation based on a protein's sequence. Their reliability across evolutionary diverse species is unknown. Here, we evaluate the performance of common algorithms (TargetP, Localizer and WoLFPSORT) for four photosynthetic eukaryotes (Arabidopsis thaliana, Zea mays, Physcomitrium patens, and Chlamydomonas reinhardtii) for which experimental plastid and mitochondrial proteome data is available, and 171 eukaryotes using orthology inferences. The match between predictions and experimental data ranges from 75% to as low as 2%. Results worsen as the evolutionary distance between training and query species increases, especially for plant mitochondria for which performance borders on random sampling. Specificity, sensitivity and precision analyses highlight cross-organelle errors and uncover the evolutionary divergence of organelles as the main driver of current performance issues. The results encourage to train the next generation of neural networks on an evolutionary more diverse set of organelle proteins for optimizing performance and reliability. Author summary: Recent advancements in genome sequencing and machine learning have been instrumental in solving numerous biological challenges, such as the prediction of the folded state of proteins from sequences alone. An intriguing cell biological challenge is tracking the localization of proteins within cells. For example, some nuclear-encoded proteins localize to mitochondria, while some are sorted to plastids. Experimentally tracking the localization of each protein across thousands of species is laborious. Instead, researchers use machine learning algorithms to predict where proteins are likely to be localized based on their sequence. How reliable are these predictions? We evaluated the reliability of prediction tools across more than a hundred plant species. We found that, as the evolutionary distance between the species used for training the algorithms and those used for testing increases, the accuracy of the predictions declines sharply. This perspective has allowed us to propose new strategies to improve these algorithms. We believe that training more distant plant genome sequences in combination with advances in artificial intelligence–and viewed through an evolutionary lens–will be crucial for developing localization prediction algorithms that are reliable across a wide range of species. [ABSTRACT FROM AUTHOR]

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Bacteroides species are abundant, prevalent, and stable members of the human gut microbiota, making them a promising chassis for developing long-term interventions for chronic diseases. Engineering Bacteroides as in situ bio-factories, however, requires efficient protein secretion tools, which are currently lacking. Here, we systematically investigate methods to enable heterologous protein secretion in Bacteroides. We identify a collection of secretion carriers that can export functional proteins across multiple Bacteroides species at high titers. To understand the mechanistic drivers of Bacteroides secretion, we characterize signal peptide sequence features, post-secretion extracellular fate, and the size limit of protein cargo. To increase titers and enable flexible control of protein secretion, we develop a strong, self-contained, inducible expression circuit. Finally, we validate the functionality of our secretion carriers in vivo in a mouse model. This toolkit promises to enable expanded development of long-term living therapeutic interventions for chronic gastrointestinal disease. Bacteroides species are a promising chassis for developing living therapeutics, but this application is challenged by their poorly understood protein secretion abilities. Here, the authors report the discovery, characterization, and implementation of a molecular toolkit that enables efficient protein secretion in Bacteroides species. [ABSTRACT FROM AUTHOR]

Background: Mitochondrial calcium uniporter (MCU) plays pleiotropic roles in cellular physiology and pathology that contributes to a variety of diseases, but the role and potential mechanism of MCU in the pathogenesis of metabolic dysfunction-associated steatohepatitis (MASH) remain poorly understood. Methods and results: Here, hepatic knockdown of MCU in C57BL/6J mice was achieved by tail vein injection of AAV8-mediated the CRISPR/Cas9. Mice were fed a Choline-deficient, L-amino acid-defined high-fat diet (CDAHFD) for 8 weeks to induce MASH and fibrosis. We find that expression of MCU enhanced in MASH livers of humans and mice. MCU knockdown robustly limits lipid droplet accumulation, steatosis, inflammation, and hepatocyte apoptotic death during MASH development both in vivo in mice and in vitro in cellular models. MCU-deficient mice strikingly mitigate MASH-related fibrosis. Moreover, the protective effects of MCU knockdown against MASH progression are accompanied by a reduced level of mitochondrial calcium, limiting hepatic oxidative stress, and attenuating mitochondrial dysfunction. Mechanically, RNA sequencing analysis and protein immunoblotting indicate that knockdown MCU inhibited the Hippo/YAP pathway activation and restored the AMP-activated protein kinase (AMPK) activity during MASH development both in vitro and in vivo. Conclusions: MCU is up-regulated in MASH livers in humans and mice; and hepatic MCU knockdown protects against diet-induced MASH and fibrosis in mice. Thus, targeting MCU may represent a novel therapeutic strategy for MASH and fibrosis. [ABSTRACT FROM AUTHOR]

Self‐assembling peptides (SAPs) are versatile building blocks in the creation of hierarchical nanostructures. Although SAPs promise precise control over assembled morphology and dimensions, experimental validation is still crucial. In this sense, recent efforts have focused on nanospiral structures, which mimic natural architectures and hold potential for biomedical and nanotechnological applications. Here, it is demonstrated that SARS‐CoV‐2 fusion peptides are able to form specific spiral‐like structures using interfacial assembly as a fabrication methodology, along with fine‐tuning of the spiral ensembles through an imposed interfacial flow due to the uniaxial compression in a Langmuir–Blodgett trough. Molecular characterization by atomic force microscopy, neutron reflectometry, interfacial shear rheology, and infrared spectroscopy, highlighted how variations in fusion peptide sequences influence assembly, highlighting the importance of molecular design. These spiral structures are subsequently modified to serve as templates for metallic replicas, expanding the potential of peptide‐guided self‐assembly in fabricating tailored nanostructured surfaces with sub‐10 nm dimensions over cm2 areas. [ABSTRACT FROM AUTHOR]

Swine acute diarrhoea syndrome coronavirus (SADS-CoV) causes vomiting, severe diarrhoea and death in newborn piglets. The spike (S) protein plays a crucial role in promoting virus invasion and inducing neutralizing antibody production. In this study, the extracellular region of the S protein was used as an immunogen to immunize BALB/c mice. After immunization, B cells were collected, fused with SP2/0 myeloma cells, cultured and subcloned, and a cell line capable of secreting neutralizing antibodies was obtained and named as 5D6. Additionally, it was determined that the 5D6 mAb could be used as the primary antibody for western blotting and indirect immunofluorescence assay (IFA) to detect SADS-CoV. Further studies indicated that the 5D6 mAb binds to the 136STSHAAD142 motif, which located in the N-terminal domain (NTD) of the spike protein. This result suggested that the NTD of the S protein can induce the production of neutralizing antibodies. Amino acid sequence alignment revealed that the epitope of the 5D6 mAb was conserved among SADS-CoV strains. This study helps elucidate the S protein function of SADS-CoV, and the 5D6 mAb may be used to develop diagnostic and treatment tools for detecting SADS-CoV infection. [ABSTRACT FROM AUTHOR]

Background: Genetic disorders could also contribute to intellectual disability. Using whole exome sequencing (WES), several variants have been identified as autosomal-dominant inheritance intellectual disability. Thus, the application of WES has demonstrated its critical role in distinguishing intellectual disability in children patients, which provides essential diagnosis and promotes therapeutic strategy. Case presentation: The proband, an 18-month-old female patient, presented with a complex clinical profile characterized by profound developmental delay, epilepsy, and neurological developmental impairment. WES identified a heterozygous c.913A>G variant in exon 2 of NACC1 , resulting in disease caused by a change in the amino acid sequence, affecting the protein features and resulting in splice site changes, as revealed by MutationTaster analysis. The protein structure of NAC1 was built and named AF-Q96RE7-F1, and the mutant site was beyond the BTB/POZ, NLS, and BEN domains. Subsequently, PyMOL software was used to illustrate the molecular structure between the wild type and the mutant type of NAC1. The residues around the 304 site of amino acid changed in NAC1 p.T304A with an altered hydrogen bond, indicating an unstable structure. The patient was diagnosed with intellectual disability and profound developmental delay with epilepsy harboring a novel de novo NACC1 variant. Upon hospital admission, a comprehensive treatment regimen was initiated, including antiseizure medications, nutritional supplements, and rehabilitation training. As a result, the patient's movement performance improved. However, recurrent epilepsy attacks still occurred. Conclusion: This is the first case revealing a novel NACC1 c.903A>G variant that induced a neurological impairment in an infant. This report expanded the understanding of the non-domain-associated variant of NACC1 and developmental disorder. [ABSTRACT FROM AUTHOR]

The ability to predict how amino acid changes affect proteins has a wide range of applications including in disease variant classification and protein engineering. Many existing methods focus on learning from patterns found in either protein sequences or protein structures. Here, we present a method for integrating information from sequence and structure in a single model that we term SSEmb (Sequence Structure Embedding). SSEmb combines a graph representation for the protein structure with a transformer model for processing multiple sequence alignments. We show that by integrating both types of information we obtain a variant effect prediction model that is robust when sequence information is scarce. We also show that SSEmb learns embeddings of the sequence and structure that are useful for other downstream tasks such as to predict protein-protein binding sites. We envisage that SSEmb may be useful both for variant effect predictions and as a representation for learning to predict protein properties that depend on sequence and structure. SSEmb is a multi-modal machine learning model that predicts how changes in a protein's amino acid sequence affect its function by combining information from a multiple sequence alignment and the three-dimensional structure. [ABSTRACT FROM AUTHOR]

Microbial bioremediation is an advanced technique for removing herbicides and heavy metals from agricultural soil. In this study, the strain Stenotrophomonas pavanii MY01 was used for its ability to degrade glyphosate, a phosphorus-containing organic compound, producing PO43− as a byproduct. PO43− is known to form stable precipitates with heavy metals, indicating that strain MY01 could potentially remove heavy metals by degrading glyphosate. Therefore, the present experiment induced phosphate precipitation from Cu(II) (Hereinafter referred to as Cu2+) and Zn(II) (Hereinafter referred to as Zn2+) by degrading glyphosate with strain MY01. Meanwhile, the whole genome of strain MY01 was mined for its glyphosate degradation mechanism and its heavy metal removal mechanism. The results of the study showed that the strain degraded glyphosate best at 34°C, pH = 7.7, and an inoculum of 0.7%, reaching 72.98% within 3d. The highest removal of Cu2+ and Zn2+ in the test was 75.95 and 68.54%, respectively. A comparison of strain MY01's genome with glyphosate degradation genes showed that protein sequences GE000474 and GE002603 had strong similarity to glyphosate oxidoreductase and C-P lyase. This suggests that these sequences may be key to the strain's ability to degrade glyphosate. The GE001435 sequence appears to be related to the phosphate pathway, which could enable phosphate excretion into the environment, where it forms stable coordination complexes with heavy metals. [ABSTRACT FROM AUTHOR]

Esophageal cancer is one of the most common cancers in the world, ranking sixth in cancer-related mortality. Doxorubicin (DOX), as a classic broad-spectrum, non-specific small-molecular anti-tumor drug, has achieved widespread use, including in the treatment of esophageal cancer. However, due to its strong cardiotoxicity, poor tumor-targeting ability, and short half-life, the clinical application of DOX has been greatly limited. In this research, we designed and successfully synthesized a peptide sequence IEIIIK (IEK for short) with excellent pH responsiveness. Under physiological conditions (pH 7.4), the peptide can encapsulate DOX and self-assemble into a stable hydrogel (DOX-IEK) through hydrophobic and electrostatic interactions. After being injected into the acidic tumor microenvironment, the protonation degree of alkaline amino acid lysine increased and the negative charge of glutamate decreased, directly leading to enhanced electrostatic repulsion and subsequent hydrogel dissociation. Released DOX can accumulate in tumor tissue and achieve anti-tumor efficacy. More importantly, the hydrogel can act as a drug reservoir for sustained drug release, improving the drug targeting ability, prolonging the duration of drug administration to compensate for the short half-life of DOX, and reducing systemic toxicity. Ideal anti-tumor efficacy has been achieved in both the in vitro and in vivo experiments. [ABSTRACT FROM AUTHOR]

Citrobacter koseri causes infection in people who are immunocompromised. Without effective antibiotics, these infections can become severe and life-threatening, so effective drugs are essential to treat these infections. Utilizing subtractive genomics, 2699 ORFs were predicted and translated into amino acid sequences. Metabolic pathway analysis and subcellular localization helped define the roles of key bacterial proteins. Two druggable proteins, WP_012000829.1 and WP_275157394.1, were discovered as promising targets. Alpha Fold provided 3D structures, and a library of 1600 echinoderm metabolites was docked against these proteins, with Ampicillin, Levofloxacin, and Doxycycline as controls. Notably, CMNPD13085 and CMNPD15632 exhibited the highest binding affinities for WP_012000829.1 and WP_275157394.1, respectively. Molecular dynamics simulations and MM-GBSA binding free energy complemented docking results. However, acknowledging the reliance on computational validations, the study emphasizes the need for essential in-vitro research to transform these potential inhibitors into therapeutic drugs. [ABSTRACT FROM AUTHOR]

The 2023 International Federation of Gynecology and Obstetrics classification with molecular classification shows superior discriminatory ability compared to staging systems lacking molecular data. However, the accuracy of endometrial biopsy data in molecular classification remains uncertain. This study aimed to assess the concordance of molecular classifications between preoperative biopsy and hysterectomy to predict prognosis before surgical staging. Endometrial biopsies and corresponding hysterectomy specimens were collected at the National Cancer Center Hospital between 2012 and 2023. Immunohistochemistry for p53 and mismatch repair (MMR) proteins and next-generation sequencing of all exons of polymerase epsilon (POLE) were performed. Given the limited number of POLE mut cases in prior studies, we prepared a POLE mut-enriched cohort. Cohen's kappa estimates were used to determine concordance for molecular and clinicopathological subgroup assignments. Among 70 patients classified into four molecular subtype groups, 33 exhibited POLE mutations, 15 showed loss of MMR protein expression, 13 had p53-abnormality, and 9 had no specific molecular profile. Concordance between biopsy and hysterectomy specimens was 100% (κ = 1.000). In contrast, histological types and grades between biopsy and surgical specimens showed moderate and substantial agreement (κ = 0.420 and κ = 0.780, respectively). Molecular subtypes were completely consistent with those derived from surgical specimens, demonstrating high concordance between preoperative and postoperative molecular classifications. This suggests that endometrial biopsies could reliably predict prognosis. Future studies should investigate how biopsy-based molecular profiling influences treatment planning and patient outcomes. • The molecular classification achieved excellent concordance with the surgical and biopsy specimens. • The concordance rate between the biopsy and surgical specimens of the 33 patients with POLE- EDM mutations was 100%. • The concordance level of the histological grade and type was consistent with previous studies. • Molecular classification can be a powerful tool that surpasses histological type and grade for preoperative diagnosis. [ABSTRACT FROM AUTHOR]

Protein domain annotation is typically done by predictive models such as HMMs trained on sequence motifs. However, sequence-based annotation methods are prone to error, particularly in calling domain boundaries and motifs within them. These methods are limited by a lack of structural information accessible to the model. With the advent of deep learning-based protein structure prediction, existing sequenced-based domain annotation methods can be improved by taking into account the geometry of protein structures. We develop dimensionality reduction methods to annotate repeat units of the Leucine Rich Repeat solenoid domain. The methods are able to correct mistakes made by existing machine learning-based annotation tools and enable the automated detection of hairpin loops and structural anomalies in the solenoid. The methods are applied to 127 predicted structures of LRR-containing intracellular innate immune proteins in the model plant Arabidopsis thaliana and validated against a benchmark dataset of 172 manually-annotated LRR domains. Author summary: In immune receptors across various organisms, repeating protein structures play a crucial role in recognizing and responding to pathogen threats. These structures resemble the coils of a slinky toy, allowing these receptors to adapt and change over time. One particularly vital but challenging structure to study is the Leucine Rich Repeat (LRR). Traditional methods that rely just on analyzing the sequence of these proteins can miss subtle changes due to rapid evolution. With the introduction of protein structure prediction tools like AlphaFold 2, annotation methods can study the coarser geometric properties of the structure. In this study, we visualize LRR proteins in three dimensions and use a mathematical approach to 'flatten' them into two dimensions, so that the coils form circles. We then used a mathematical concept called winding number to determine the number of repeats and where they are in a protein sequence. This process helps reveal their repeating patterns with enhanced clarity. When we applied this method to immune receptors from a model plant organism, we found that our approach could accurately identify coiling patterns. Furthermore, we detected errors made by previous methods and highlighted unique structural variations. Our research offers a fresh perspective on understanding immune receptors, potentially influencing studies on their evolution and function. [ABSTRACT FROM AUTHOR]

Diabetes mellitus (DM) is a metabolic syndrome characterized by hyperglycaemia, polydipsia, polyuria, and weight loss, among others. The pathophysiology for the disorders is complex and results in pancreatic abnormal function. Viruses have also been implicated in the metabolic syndrome. This study charted peptides to investigate and predict the autoimmune potential of shared sequences between 8 viral species proteins (which we termed 'diabetes-viruses') and the human pancreatic proteins. The structure and immunological relevance of shared sequences between viruses reported in DM onset and human pancreatic proteins were analysed. At nonapeptide mapping between human pancreatic protein and 'diabetic-viruses', reveal 1064 shared sequences distributed among 454 humans and 4288 viral protein sequences. The viral results showed herpesviruses, enterovirus (EV), human endogenous retrovirus, influenza A viruses, rotavirus, and rubivirus sequences are hosted by the human pancreatic protein. The most common shared nonapeptide was AAAAAAAAA, present in 30 human nonredundant sequences. Among the viral species, the shared sequence NSLEVLFQG occurred in 18 nonredundant EVs protein, while occurring merely in 1 human protein, whereas LGLDIEIAT occurred in 8 influenza A viruses overlapping to 1 human protein and KDELSEARE occurred in 2 rotaviruses. The prediction of the location of the shared sequences in the protein structures, showed most of the shared sequences are exposed and located either on the surface or cleft relative to the entire protein structure. Besides, the peptides in the viral protein shareome were predicted computationally for binding to MHC molecules. Here analyses showed that the entire 1064 shared sequences predicted 203 to be either HLA-A or B supertype-restricted epitopes. Fifty-one of the putative epitopes matched reported HLA ligands/T-cell epitopes majorly coming from EV B supertype representative allele restrictions. These data, shared sequences, and epitope charts provide important insight into the role of viruses on the onset of DM and its implications. [ABSTRACT FROM AUTHOR]

Proteoforms are proteins derived from highly related genes or post translational modifications (PTMs) of the same protein. They share extremely similar primary structures but have varying functions. Unfortunately, protein de novo sequencing including specific PTM/mutation detection is still challenging. Herein, a nanopore‐based technique is reported to resolve the amino acid order of amyloid‐β (Aβ1‐42) with site specificity. Subnanopores are sputtered in 5 nm‐thick inorganic membranes with a sensing depth of 0.66 nm inferred by finite element analysis. Denatured molecules at 0.45 ng mL−1 translocate through subnanopores while the current traces are sampled at 500 kHz with rms noise <15 pA. Hundreds of blockades are clustered using machine learning, and multiple blockades are averaged to establish current consensus. Consensus traces strongly correlate with a linear model of amino acid volume of Aβ1‐42 at single residue resolution, with Pearson Correlation Coefficients (PCCs) of 0.81 ± 0.03 and 0.92 ± 0.03 before and after dynamic time warping (DTW). A scrambled version of Aβ1‐42 is tested for validation purposes. Deep learning classification reveals that different polypeptides generate distinct translocation fluctuating patterns, but variations become imperceptible for the same species measured across nanopores (Area Under the Curve, AUC 0.93 ± 0.05 vs 0.64 ± 0.12). Lastly, important PTMs and mutations are site‐specifically located along the primary structure, implying new potential clinical applications. [ABSTRACT FROM AUTHOR]

Background: The prediction of protein–protein interaction sites plays a crucial role in biochemical processes. Investigating the interaction between viruses and receptor proteins through biological techniques aids in understanding disease mechanisms and guides the development of corresponding drugs. While various methods have been proposed in the past, they often suffer from drawbacks such as long processing times, high costs, and low accuracy. Results: Addressing these challenges, we propose a novel protein–protein interaction site prediction network based on multi-information fusion. In our approach, the initial amino acid features are depicted by the position-specific scoring matrix, hidden Markov model, dictionary of protein secondary structure, and one-hot encoding. Simultaneously, we adopt a multi-channel approach to extract deep-level amino acids features from different perspectives. The graph convolutional network channel effectively extracts spatial structural information. The bidirectional long short-term memory channel treats the amino acid sequence as natural language, capturing the protein's primary structure information. The ProtT5 protein large language model channel outputs a more comprehensive amino acid embedding representation, providing a robust complement to the two aforementioned channels. Finally, the obtained amino acid features are fed into the prediction layer for the final prediction. Conclusion: Compared with six protein structure-based methods and six protein sequence-based methods, our model achieves optimal performance across evaluation metrics, including accuracy, precision, F1, Matthews correlation coefficient, and area under the precision recall curve, which demonstrates the superiority of our model. [ABSTRACT FROM AUTHOR]

Introduction: Flesh color is an essential trait in chieh-qua (Benincasa hispida Cogn. var. Chieh-qua How); however, the inheritance and molecular basis of green flesh trait remain unclear. Methods: In the present study, two F2 populations, derived from 1742 (white flesh) × FJ3211 (green flesh) and J16 (white flesh) × FJ5 (green flesh), were used to identify the green flesh (Cqgf) locus. Results: Genetic analysis revealed that the presence of green flesh was a quantitative trait that closely followed a normal distribution. Combining the results from QTL mapping and BSA-seq analysis, the Cqgf locus was preliminarily determined to be located on chromosome 05 and was narrowed down to a 2.55-Mb interval by linkage analysis. A large J16 × FJ5 F2 population comprising 3,180 individuals was subsequently used to screen the recombinants, and the Cqgf locus was fine-mapped to a region of 329.70 kb that harbors six genes. One of the candidate genes, Bch05G003700 , the zinc-finger transcription factor LOL1 (lsd one like 1 protein; CqLOL1), was the strongest candidate gene for the Cqgf locus according to sequence variation and expression analysis. Additionally, a point mutation (A > C) in CqLOL1 resulted in the substitution of threonine (T) with proline (P) in the amino acid sequence, showing a complete relationship linked with flesh color in a panel of 45 germplasms. Discussion: The study suggests that CqLOL1 promotes the accumulation of chlorophyll content in chieh-qua and lead to green flesh. Our findings establish a theoretical and technical foundation for breeding different flesh color lines and elucidating the underlying mechanisms of flesh color in chieh-qua. [ABSTRACT FROM AUTHOR]

Introduction: Antibodies represent a specific class of proteins produced by the adaptive immune system in response to pathogens. Mining the information embedded in antibody amino acid sequences can benefit both antibody property prediction and novel therapeutic development. However, antibodies possess unique features that should be incorporated using specifically designed training methods, leaving room for improvement in pre-training models for antibody sequences. Methods: In this study, we present a Pre-trained model of Antibody sequences trained with a Rational Approach for antibodies (PARA). PARA employs a strategy conforming to antibody sequence patterns and an advanced natural language processing self-encoding model structure. This approach addresses the limitations of existing protein pre-training models, which primarily utilize language models without fully considering the differences between protein sequences and language sequences. Results: We demonstrate PARA's performance on several tasks by comparing it to various published pre-training models of antibodies. The results show that PARA significantly outperforms existing models on these tasks, suggesting that PARA has an advantage in capturing antibody sequence information. Discussion: The antibody latent representation provided by PARA can substantially facilitate studies in relevant areas. We believe that PARA's superior performance in capturing antibody sequence information offers significant potential for both antibody property prediction and the development of novel therapeutics. PARA is available at https://github.com/xtalpi-xic. [ABSTRACT FROM AUTHOR]

Blastomyces dermatitidis and Blastomyces gilchristii are cryptic species of fungi that cause blastomycosis, an often severe disease involving pulmonary infection capable of systemic dissemination. While these species appear morphologically identical, differences exist in the genetic makeup, geographical range, and possibly the clinical presentation of infection. Here, we show genetic divergence between the cryptic species through both a Blastomyces species tree constructed from orthologous protein sequences and whole genome single-nucleotide variant phylogenomic analysis. Following linked-read sequencing and de novo genome assembly, we characterized and compared the genomes of 3 B. dermatitidis and 3 B. gilchristii isolates. The B. gilchristii genomes (73.25–75.4 Mb) were ∼8 Mb larger than the B. dermatitidis genomes (64.88–66.61 Mb). Average nucleotide identity was lower between genomes of different species than genomes of the same species, yet functional classification of genes suggested similar proteomes. The most striking difference involved long terminal repeat retrotransposons. Although the same retrotransposon elements were detected in the genomes, the quantity of elements differed between the 2 species. Gypsy retrotransposon content was significantly higher in B. gilchristii (38.04–39.26 Mb) than in B. dermatitidis (30.85–32.40 Mb), accounting for the majority of genome size difference between species. Age estimation and phylogenetic analysis of the reverse transcriptase domains suggested that these retrotransposons are relatively ancient, with genome insertion predating the speciation of B. dermatitidis and B. gilchristii. We postulate that different trajectories of genome contraction led to genetic incompatibility, reproductive isolation, and speciation, highlighting the role of transposable elements in fungal evolution. [ABSTRACT FROM AUTHOR]

Conventional chemotherapeutic agents are limited by their lack of targeting and penetration and their short retention time, and chemotherapy might induce an immune suppressive environment. Peptide self‐assembly can result in a specific morphology, and the resulting morphological changes are stimuli responsive to the external environment, which is important for drug permeation and retention of encapsulated chemotherapeutic agents. In this study, a polypeptide (Pep1) containing the peptide sequences PLGLAG and RGD that is responsive to matrix metalloproteinase 2 (MMP‐2) was successfully developed. Pep1 underwent a morphological transformation from a spherical structure to aggregates with a high aspect ratio in response to MMP‐2 induction. This drug delivery system (DI/Pep1) can transport doxorubicin (DOX) and indomethacin (IND) simultaneously to target tumor cells for subsequent drug release while extending drug retention within tumor cells, which increases immunogenic cell death and facilitates the immunotherapeutic effect of CD4+ T cells. Ultimately, DI/Pep1 attenuated tumor‐associated inflammation, enhanced the body's immune response, and inhibited breast cancer growth by combining the actions of DOX and IND. Our research offers an approach to hopefully enhance the effectiveness of cancer treatment. [ABSTRACT FROM AUTHOR]

Existing deep learning methods have shown outstanding performance in predicting drug–target interactions. However, they still have limitations: (1) the over-reliance on locally extracted features by some single encoders, with insufficient consideration of global features, and (2) the inadequate modeling and learning of local crucial interaction sites in drug–target interaction pairs. In this study, we propose a novel drug–target interaction prediction model called the Neural Fingerprint and Self-Attention Mechanism (NFSA-DTI), which effectively integrates the local information of drug molecules and target sequences with their respective global features. The neural fingerprint method is used in this model to extract global features of drug molecules, while the self-attention mechanism is utilized to enhance CNN's capability in capturing the long-distance dependencies between the subsequences in the target amino acid sequence. In the feature fusion module, we improve the bilinear attention network by incorporating attention pooling, which enhances the model's ability to learn local crucial interaction sites in the drug–target pair. The experimental results on three benchmark datasets demonstrated that NFSA-DTI outperformed all baseline models in predictive performance. Furthermore, case studies illustrated that our model could provide valuable insights for drug discovery. Moreover, our model offers molecular-level interpretations. [ABSTRACT FROM AUTHOR]

Laccase is an enzyme that plays an important role in fungi, including lignin degradation, stress defense, and formation of fruiting bodies. Auricularia auricula-judae is a white-rot fungus in the Basidiomycota phylum, capable of delignifying wood. In this study, seven genes belonging to the laccase family were identified through de novo sequencing, containing Cu-Oxidase, Cu-Oxidase_2, and Cu-Oxidase_3 domains. Subsequently, the physical characteristics, phylogenetic relationships, protein secondary structure, and tertiary structure of the laccase family (AaLac1–AaLac7) were analyzed. Prediction of N-glycosylation sites identified 2 to 10 sites in the laccase family, with AaLac7 having the highest number of sites at 10. Sequence alignment and analysis of the laccase family showed high consistency in signature sequences. Phylogenetic analysis confirmed the relationship among laccases within the family, with AaLac3–AaLac4 and AaLac5–AaLac6 being closely positioned on the tree, exhibiting high similarity in tertiary structure predictions. This study identified and analyzed laccase family genes in Auricularia auricula-judae using de novo sequencing, offering a simple method for identifying and analyzing the laccase family in organisms with unknown genetic information. [ABSTRACT FROM AUTHOR]

Previously, Segev and Gerst found that mutants in any of the four ribosomal protein genes rpl1b, rpl2b, rps11a, or rps26b had a petite phenotype—i.e., the mutants were deficient in respiration. Strikingly, mutants of their paralogs rpl1a, rpl2a, rps11b, and rps26a were grande—i.e., competent for respiration. It is remarkable that these paralogs should have opposite phenotypes, because three of the paralog pairs (Rpl1a/Rpl1b, Rpl2a/Rpl2b, Rps11a/Rps11b) are 100% identical to each other in terms of their amino acid sequences, while Rps26a and Rps26b differ in 2 amino acids out of 119. However, while attempting to use this paralog-specific petite phenotype in an unrelated experiment, I found that the rpl1b, rpl2b, rps11a, and rps26b deletion mutants are competent for respiration, contrary to the findings of Segev and Gerst. [ABSTRACT FROM AUTHOR]

Coriolopsis gallica (Cga) is a white-rot fungus renowned for its ability to secrete ligninolytic enzymes that are capable of oxidizing phenolic compounds. This study aimed to investigate the biochemical characteristics of a dye-decolorizing peroxidase named CgaDyP1 and test its ability to biotransform antibiotics. CgaDyP1 was cloned and heterologously expressed in Escherichia coli. We fully characterized the biochemical properties of CgaDyP1 and evaluated its dye-decolorizing potential to confirm that it belongs to the DyP class of enzymes. We also tested its fluoroquinolone antibiotic biotransformation potential for possible biotechnological applications. Alignment of the primary amino acid sequence with DyP homolog sequences showed that CgaDyP1 has high similarity with other fungal DyPs. The recombinant CgaDyP1 exhibited activity on substrates such as ABTS and 2,6-dimethoxyphenol (DMP) with optimal performance at a pH of 3, although activity at pH 2.5, pH 4, and pH5 diminished over time. Thermostability tests indicated that the enzyme remains stable at temperatures between 30 °C and 50 °C and retains 70% of its initial activity after 180 min at 50 °C. Tests on the effect of hydrogen peroxide on CgaDyP1 activity found peak activity at 0.25 mM H2O2. CgaDyP1 decolorized five industrial dyes, and kinetics data confirmed that it belongs to the DyP class of enzymes. CgaDyP1 was shown to biotransform some of the 7 recalcitrant fluoroquinolone antibiotics tested here, including levofloxacin, moxifloxacin, and norfloxacin, and thus holds potential for biotechnological applications. [ABSTRACT FROM AUTHOR]

Background: The maximal sensitivity for local pairwise alignment makes the Smith-Waterman algorithm a popular choice for protein sequence database search. However, its quadratic time complexity makes it compute-intensive. Unfortunately, current state-of-the-art software tools are not able to leverage the massively parallel processing capabilities of modern GPUs with close-to-peak performance. This motivates the need for more efficient implementations. Results: CUDASW++4.0 is a fast software tool for scanning protein sequence databases with the Smith-Waterman algorithm on CUDA-enabled GPUs. Our approach achieves high efficiency for dynamic programming-based alignment computation by minimizing memory accesses and instructions. We provide both efficient matrix tiling, and sequence database partitioning schemes, and exploit next generation floating point arithmetic and novel DPX instructions. This leads to close-to-peak performance on modern GPU generations (Ampere, Ada, Hopper) with throughput rates of up to 1.94 TCUPS, 5.01 TCUPS, 5.71 TCUPS on an A100, L40S, and H100, respectively. Evaluation on the Swiss-Prot, UniRef50, and TrEMBL databases shows that CUDASW++4.0 gains over an order-of-magnitude performance improvements over previous GPU-based approaches (CUDASW++3.0, ADEPT, SW#DB). In addition, our algorithm demonstrates significant speedups over top-performing CPU-based tools (BLASTP, SWIPE, SWIMM2.0), can exploit multi-GPU nodes with linear scaling, and features an impressive energy efficiency of up to 15.7 GCUPS/Watt. Conclusion: CUDASW++4.0 changes the standing of GPUs in protein sequence database search with Smith-Waterman alignment by providing close-to-peak performance on modern GPUs. It is freely available at https://github.com/asbschmidt/CUDASW4. [ABSTRACT FROM AUTHOR]

Lignin is a phenolic polymer that is a major source of biomass. Oxidative enzymes, such as laccase and peroxidase, are required for lignin polymerisation. Laccase is a member of the multicopper oxidase family and has a high amino acid sequence similarity with ascorbate oxidase. However, the process of functional differentiation between the two enzymes remains poorly understood. In this study, the common ancestry sequence of laccase and ascorbate oxidase (AncMCO) was predicted via phylogenetic reconstruction, and its in vivo effect on lignin biosynthesis in Arabidopsis thaliana was assessed. The estimated AncMCO sequence conserved key residues that coordinate with copper ions, implying that the electron transfer system is likely to be conserved in AncMCO. However, multiple insertions/deletions corresponding to protein surface structures have been found between laccase, ascorbate oxidase, and AncMCO. The overexpression of canonical laccase (AtLAC4) and ascorbate oxidase (AtAAO1) in A. thaliana resulted in notable increases of syringyl/guaiacyl lignin unit ratio in stems, whereas, in contrast, the overexpression of AncMCO did not show any detectable change in lignin deposition. Transcriptomic analysis revealed that the AtAAO1-overexpressing line exhibited significant changes in the expression of a wide range of cell wall biosynthesis genes. These results highlight the importance of the molecular evolution of multicopper oxidase, which drives lignin biosynthesis during plant evolution. [ABSTRACT FROM AUTHOR]

One of the first cellular locations of the calreticulin (CRT) chaperone in eukaryotic cells, apart from its obvious localization in the endoplasmic reticulum (ER), was the cell nucleus (Opas et al. 1991). The presence of CRT has been detected inside the nucleus and in the nuclear envelope of animal and plant cells, and a putative nuclear localization signal (NLS) in the CRT amino acid sequence has been mapped in several animal and plant species. Over the last 30 years, other localization sites of this protein outside the ER and cell nucleus have also been discovered, suggesting that CRT is a multifunctional Ca2+-binding protein widely found in various cell types. In our previous studies focusing on plant developmental biology, we have demonstrated the presence of CRT inside and outside the ER in highly specialized plant cells, as well as the possibility of CRT localization in the cell nucleus. In this paper, we present a detailed analysis of immunocytochemical localization of CRT inside nuclei of the pistil transmission tract somatic cells before and after pollination. We show a similar pattern of the nuclear CRT localization in relation to exchangeable Ca2+ for two selected species of angiosperms, dicotyledonous Petunia and monocot Haemanthus, that differ in anatomical structure of the pistil and discuss the potential role of CRT in the cell nucleus. [ABSTRACT FROM AUTHOR]

Summary: For several decades, a plant‐based expression system has been proposed as an alternative platform for the production of biopharmaceuticals including therapeutic monoclonal antibodies (mAbs), but the immunogenicity concerns associated with plant‐specific N‐glycans attached in plant‐based biopharmaceuticals has not been completely solved. To eliminate all plant‐specific N‐glycan structure, eight genes involved in plant‐specific N‐glycosylation were mutated in rice (Oryza sativa) using the CRISPR/Cas9 system. The glycoengineered cell lines, PhytoRice®, contained a predominant GnGn (G0) glycoform. The gene for codon‐optimized trastuzumab (TMab) was then introduced into PhytoRice® through Agrobacterium co‐cultivation. Selected cell lines were suspension cultured, and TMab secreted from cells was purified from the cultured media. The amino acid sequence of the TMab produced by PhytoRice® (P‐TMab) was identical to that of TMab. The inhibitory effect of P‐TMab on the proliferation of the BT‐474 cancer cell line was significantly enhanced at concentrations above 1 μg/mL (****P < 0.0001). P‐TMab bound to a FcγRIIIa variant, FcγRIIIa‐F158, more than 2.7 times more effectively than TMab. The ADCC efficacy of P‐TMab against Jurkat cells was 2.6 times higher than that of TMab in an in vitro ADCC assay. Furthermore, P‐TMab demonstrated efficient tumour uptake with less liver uptake compared to TMab in a xenograft assay using the BT‐474 mouse model. These results suggest that the glycoengineered PhytoRice® could be an alternative platform for mAb production compared to current CHO cells, and P‐TMab has a novel and enhanced efficacy compared to TMab. [ABSTRACT FROM AUTHOR]

In this work, we conceived and developed antibody-drug conjugates (ADCs) that could efficiently release the drug after enzymatic cleavage of the linker moiety by tumoral proteases. The antibody-drug linkers we used are the result of a rational optimization of a previously reported PEGylated linker, PUREBRIGHT® MA-P12-PS, which showed excellent drug loading capacities but lacked an inbuilt drug discharge mechanism, thus limiting the potency of the resulting ADCs. To address this limitation, we chose to incorporate a protease-sensitive trigger into the linker to favor the release of a "PEGless" drug inside the tumor cells and, therefore, obtain potent ADCs. Currently, most marketed ADCs are based on the Val-Cit dipeptide followed by a self-immolative spacer for releasing the drug in its unmodified form. Here, we selected two untraditional peptide sequences, a Phe-Gly dipeptide and a Val-Ala-Gly tripeptide and placed one or the other in between the drug on one side (N-terminus) and the rest of the linker, including the PEG moiety, on the other side (C-terminus), without a self-immolative group. We found that both linkers responded to cathepsin B, a reference lysosomal enzyme, and liberated a PEG-free drug catabolite, as desired. We then used the two linkers to generate ADCs based on trastuzumab (a HER2-targeting antibody) and DM1 (a microtubule-targeted cytotoxic agent) with an average drug-to-antibody ratio (DAR) of 4 or 8. The ADCs showed restored cytotoxicity in vitro , which was proportional to the DM1 loading and generally higher for the ADCs bearing Val-Ala-Gly in their structure. In an ovarian cancer mouse model, the DAR 8 ADC based on Val-Ala-Gly behaved better than Kadcyla® (an approved ADC of DAR 3.5 used as control throughout this study), leading to a higher tumor volume reduction and more prolonged median survival. Taken together, our results depict a successful linker optimization process and encourage the application of the Val-Ala-Gly tripeptide as an alternative to other existing protease-sensitive triggers for ADCs. [Display omitted] • Hydrophilic linkers are necessary to accommodate high drug loadings on antibodies. • The proposed linker features two pendant PEG arms to create a hydrated shell around the drug. • The proposed linker uses a protease-sensitive trigger for controlled "PEGless" drug release. • The linker optimization process led to a DAR 8 ADC active in a xenograft tumor model. [ABSTRACT FROM AUTHOR]

This study aimed to identify the specific vaccine strain associated with herpes zoster (HZ) in children following a series of diagnosed cases and to explore whether differences in single nucleotide polymorphisms (SNPs) among various vaccine strains are linked to an increased incidence of herpes zoster after vaccination. From February 2021 to March 2024, children <12 years old suspected of vaccine-related varicella-like rash or HZ were included. Varicella zoster virus DNA isolated from the patients were sequenced to differentiate vaccine type versus wild-type. 3D protein structures of pORF62 were simulated using open reading frame 62 sequences extracted from whole genome sequencing of vOka, MAV/06, Oka/SK vaccines, and pOka reference. A total of 27 children with a median age of 2.1 (interquartile range, 1.5–3.4) years old presented with vaccine-related varicella-like rash (n = 4/27, 14.8%) or HZ (n = 23/27, 85.2%). One patient with varicella-like rash and 34.8% (n = 8/23) with HZ had disseminated skin involvement. All were immunized with the Oka/SK strain varicella vaccine. Genotyping showed 88.2% (n = 15/17) had SNPs specific to the Oka/SK strain, and two had SNPs considered pOka type contained within the Oka/SK vaccine. Despite accumulations of SNPs in ORF 62 of Oka/SK, the translated amino acid sequence and 3D protein structure were identical to wild-type pOka's pORF62. In vOKA and MAV/06, changes in amino acids occurred at two positions, S628G and R958G, within pORF62. The predicted 3D protein structure of vOka and MAV/06's pORF62 showed that the α helical structure within region I undergoes conformational change, potentially increasing difficulties in interactions with infection-related proteins and thereby decreasing virulence. pORF62 in pOka and Oka/SK exhibited more stable structure complex of the α helical structure. Lack of structural alternations in region I of pORF62 due to the absence of critical genetic polymorphisms in open reading frame 62 could be associated with the heightened incidence of adverse events. [ABSTRACT FROM AUTHOR]

Minimal residual disease (MRD) status in multiple myeloma (MM) is an important prognostic biomarker. Personalized blood-based targeted mass spectrometry detecting M-proteins (MS-MRD) was shown to provide a sensitive and minimally invasive alternative to MRD-assessment in bone marrow. However, MS-MRD still comprises of manual steps that hamper upscaling of MS-MRD testing. Here, we introduce a proof-of-concept for a novel workflow using data independent acquisition-parallel accumulation and serial fragmentation (dia-PASEF) and automated data processing. Using automated data processing of dia-PASEF measurements, we developed a workflow that identified unique targets from MM patient sera and personalized protein sequence databases. We generated patient-specific libraries linked to dia-PASEF methods and subsequently quantitated and reported M-protein concentrations in MM patient follow-up samples. Assay performance of parallel reaction monitoring (prm)-PASEF and dia-PASEF workflows were compared and we tested mixing patient intake sera for multiplexed target selection. No significant differences were observed in lowest detectable concentration, linearity, and slope coefficient when comparing prm-PASEF and dia-PASEF measurements of serial dilutions of patient sera. To improve assay development times, we tested multiplexing patient intake sera for target selection which resulted in the selection of identical clonotypic peptides for both simplex and multiplex dia-PASEF. Furthermore, assay development times improved up to 25× when measuring multiplexed samples for peptide selection compared to simplex. Dia-PASEF technology combined with automated data processing and multiplexed target selection facilitated the development of a faster MS-MRD workflow which benefits upscaling and is an important step towards the clinical implementation of MS-MRD. [ABSTRACT FROM AUTHOR]

BACKGROUND: Sturgeon cartilage type II collagen peptides (SHCPs) can self‐assemble and be used to prepare collagen peptide assemblies. Self‐assembled peptides have great potential for applications in the food industry. In the present study, self‐assembled peptides were prepared from sturgeon cartilage and then characterized. RESULTS: The SHCPs self‐assembled and formed collagen peptide assemblies. After response surface experiment optimization, the optimal enzyme digestion process comprised 43.1 °C, 3.37 h and 0.96% enzyme addition, and the peptide yield was 78.46%. Physicochemical analysis showed that the SHCPs were amphiphilic, with an average molecular weight of 1081 Da, and were rich in hydrophobic amino acids. Peptide sequence identification showed that the peptides of SHCPs with polar amino acids followed by hydrophobic amino acids could be self‐assembled through hydrogen bonding and hydrophobic interaction. Through turbidity experiments, Fourier transform infrared spectroscopy and scanning electron microscopy, we demonstrated that SHCPs can self‐assemble into reticular and tubular structures under specific conditions. Furthermore, both the SHCPs‐Ca and SHCPs‐Mg assemblies were stabilized within a pH range consistent with that of the human gastrointestinal tract. CONCLUSION: The present study provides a simple and safe method for preparing novel self‐assembled peptide materials from sturgeon by‐products, providing a scientific basis for the exploitation of sturgeon cartilage and potentially reducing resource wastage. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

With urinary proteomics profiling (UPP) as exemplary omics technology, this review describes a workflow for the analysis of omics data in large study populations. The proposed workflow includes: (i) planning omics studies and sample size considerations; (ii) preparing the data for analysis; (iii) preprocessing the UPP data; (iv) the basic statistical steps required for data curation; (v) the selection of covariables; (vi) relating continuously distributed or categorical outcomes to a series of single markers (e.g., sequenced urinary peptide fragments identifying the parental proteins); (vii) showing the added diagnostic or prognostic value of the UPP markers over and beyond classical risk factors, and (viii) pathway analysis to identify targets for personalized intervention in disease prevention or treatment. Additionally, two short sections respectively address multiomics studies and machine learning. In conclusion, the analysis of adverse health outcomes in relation to omics biomarkers rests on the same statistical principle as any other data collected in large population or patient cohorts. The large number of biomarkers, which have to be considered simultaneously requires planning ahead how the study database will be structured and curated, imported in statistical software packages, analysis results will be triaged for clinical relevance, and presented. [ABSTRACT FROM AUTHOR]

Porcine epidemic diarrhea virus (PEDV) causes the third most important disease in the pig industry, after African swine fever and porcine reproductive and respiratory syndrome, and leads to illness or death of the entire litter, causing significant economic losses. In this study, three PEDV strains (HN-1, HN-2, and SC2023) were isolated from swine farms with suspected PEDV infections in Sichuan and Henan provinces. Phylogenetic analysis based on complete S gene sequences showed that all three strains belonged to the G2c subgroup. HN-1 adapted readily to cell culture, grew to a viral titer as high as 2 × 108 TCID50/mL in Vero cells, and caused the formation of large syncytia. We analyzed the amino acid sequence of the HN-1 isolate and found that its S1 subunit contained a three-amino-acid insertion (355KRL358). A seven-amino-acid-deletion (1377FEKVHVQ1383) in the S2 subunit resulted in the partial deletion of the endocytosis signal YxxΦ and the complete deletion of the endoplasmic reticulum retrieval signal (ERRS) KVHVQ in the cytoplasmic tail of the S protein. Consequently, HN-1 is predicted to be less pathogenic than its parent strain, an attribute that facilitates rapid cell-to-cell spread by enhancing syncytium formation. In addition, strain HN-1 was found to have the mutation 884–885SG→RR, which may favor adaptation to cell culture by providing new trypsin cleavage sites. These results suggest that HN-1 is a G2c subtype variant that adapts well to cell culture and can be used to study the adaptive mechanisms of PEDV and develop attenuated vaccines. [ABSTRACT FROM AUTHOR]

In this study, we have demonstrated a complementary-determining region (CDR) grafting technology for the generation of rabbit scFvs with different antigen recognition and physicochemical properties. The antigen-binding affinity of the CDR-grafted anti-CRP scFv, C1R/B1R (V1), which was generated by the CDR/framework region (CDR/FR) definition based on the traditional numbering rule, was insufficient when compared to that of the original clone, C1R, suggesting that the amino acid residues outside the original CDRs might significantly contribute to antigen recognition in rabbit scFvs. We redefined new CDRs and FRs to maintain antigen-binding affinities through the extension of multiple amino acid residues for CDRH1 and CDRH2, based on the amino acid sequence alignments of rabbit scFvs isolated from phage libraries. The new version successfully maintained the antigen binding affinity. CDR-grafted scFvs possessing a common CDR sequence and different FR sequences were successfully generated based on this new CDR/FR definition, and their physicochemical properties were further investigated. The antigen-binding activities of rabbit scFvs on Maxisorp varied between the tested clones in sandwich ELISA, supporting the idea that the combination of CDR with different FRs might change the physicochemical properties of scFvs on a solid material. The CDR-grafted scFvs possessing a frame sequence of anti-CRP scFv C2R maintained the ability to bind to protein L and were successfully purified. Expression titers showed improved solubility by diminishing the amount of insoluble scFvs. Thus, the method developed in this study is promising for generating alternatives with strict antigen binding recognition and different physicochemical properties. [Display omitted] [ABSTRACT FROM AUTHOR]