Amino Acid Sequence Of Protein Research Articles

Complete genome sequence and phylogenetic analysis of a newly discovered fusagra-like virus infecting Carica papaya in Ecuador.

A new fusagra-like virus infecting papaya (Carica papaya L.) was genetically characterized. The genome of the virus, provisionally named "papaya sticky fruit-associated virus" (PSFaV), is a single molecule of double-stranded RNA, 9,199 nucleotides (nt) in length, containing two discontinuous open reading frames. Pairwise sequence comparisons based on complete RNA-dependent-RNA-polymerase (RdRp) sequences revealed identity of 79.4% and 83.3% at the nt and amino acid (aa) level, respectively, to babaco meleira-like virus (BabMelV), an uncharacterized virus sequence discovered in babaco (Vasconcellea x heilbornii) in Ecuador. Additional plant-associated viruses with sequence identity in the 50% range included papaya meleira virus (PMeV) isolates from Brazil. Phylogenetic analysis based on the amino acid sequences of the capsid protein (CP), RdRp, and CP-RdRp fusion protein genes placed PSFaV in a group within a well-supported clade that shares a recent ancestor with Sclerotium rolfsii RNA virus 2 and Phlebiopsis gigantea mycovirus dsRNA 2, two fungus-associated fusagraviruses. Genomic features and phylogenetic relatedness suggest that PSFaV, along with its closest relative BabMelV, represent a species of novel plant-associated virus classified within the recently established family Fusagraviridae.

Tandam mass spectrometry instrumentation and application in pharmaceutical analysis

Proteins and peptides can be analyzed using mass spectrometry (MS) using a range of techniques, including matrix-aided laser desorption ionization-mass spectrometry (MALDI-MS) and electrospray ionization-mass spectrometry (ESI-MS). These techniques make it possible to determine a protein's mass as an intact molecule or to identify a protein using peptide-mass fingerprinting that is produced during enzymatic digestion. The amino acid sequence of proteins (top-down and middle-down proteomics) and peptides (bottom-up proteomics) can be ascertained by fragmenting the proteins and peptides using tandem mass spectrometry (MS/MS). Furthermore, post-translational modifications (PTMs) of proteins and peptides can be identified using tandem mass spectrometry. In this article, we go over the use of MS/MS in biomedical research and provide concrete examples of how to identify proteins, peptides, and their PTMs as useful biomarkers for diagnosis and treatment. In numerous applications, tandem mass spectrometry (MS/MS) has shown to be a practical and efficient analytical method for the direct detection of target compounds in food samples. It combines the power of MS/MS as an identification and confirmation approach with the separation capabilities of chromatography when used with chromatographic techniques.

Mechanisms and Delivery of tRNA Therapeutics.

Transfer ribonucleic acid (tRNA) therapeutics will provide personalized and mutation specific medicines to treat human genetic diseases for which no cures currently exist. The tRNAs are a family of adaptor molecules that interpret the nucleic acid sequences in our genes into the amino acid sequences of proteins that dictate cell function. Humans encode more than 600 tRNA genes. Interestingly, even healthy individuals contain some mutant tRNAs that make mistakes. Missense suppressor tRNAs insert the wrong amino acid in proteins, and nonsense suppressor tRNAs read through premature stop signals to generate full length proteins. Mutations that underlie many human diseases, including neurodegenerative diseases, cancers, and diverse rare genetic disorders, result from missense or nonsense mutations. Thus, specific tRNA variants can be strategically deployed as therapeutic agents to correct genetic defects. We review the mechanisms of tRNA therapeutic activity, the nature of the therapeutic window for nonsense and missense suppression as well as wild-type tRNA supplementation. We discuss the challenges and promises of delivering tRNAs as synthetic RNAs or as gene therapies. Together, tRNA medicines will provide novel treatments for common and rare genetic diseases in humans.

PDB NextGen Archive: centralizing access to integrated annotations and enriched structural information by the Worldwide Protein Data Bank.

The Protein Data Bank (PDB) is the global repository for public-domain experimentally determined 3D biomolecular structural information. The archival nature of the PDB presents certain challenges pertaining to updating or adding associated annotations from trusted external biodata resources. While each Worldwide PDB (wwPDB) partner has made best efforts to provide up-to-date external annotations, accessing and integrating information from disparate wwPDB data centers can be an involved process. To address this issue, the wwPDB has established the PDB Next Generation (or NextGen) Archive, developed to centralize and streamline access to enriched structural annotations from wwPDB partners and trusted external sources. At present, the NextGen Archive provides mappings between experimentally determined 3D structures of proteins and UniProt amino acid sequences, domain annotations from Pfam, SCOP2 and CATH databases and intra-molecular connectivity information. Since launch, the PDB NextGen Archive has seen substantial user engagement with over 3.5 million data file downloads, ensuring researchers have access to accurate, up-to-date and easily accessible structural annotations. Database URL: http://www.wwpdb.org/ftp/pdb-nextgen-archive-site.

A multimodal Transformer Network for protein-small molecule interactions enhances predictions of kinase inhibition and enzyme-substrate relationships.

The activities of most enzymes and drugs depend on interactions between proteins and small molecules. Accurate prediction of these interactions could greatly accelerate pharmaceutical and biotechnological research. Current machine learning models designed for this task have a limited ability to generalize beyond the proteins used for training. This limitation is likely due to a lack of information exchange between the protein and the small molecule during the generation of the required numerical representations. Here, we introduce ProSmith, a machine learning framework that employs a multimodal Transformer Network to simultaneously process protein amino acid sequences and small molecule strings in the same input. This approach facilitates the exchange of all relevant information between the two molecule types during the computation of their numerical representations, allowing the model to account for their structural and functional interactions. Our final model combines gradient boosting predictions based on the resulting multimodal Transformer Network with independent predictions based on separate deep learning representations of the proteins and small molecules. The resulting predictions outperform recently published state-of-the-art models for predicting protein-small molecule interactions across three diverse tasks: predicting kinase inhibitions; inferring potential substrates for enzymes; and predicting Michaelis constants KM. The Python code provided can be used to easily implement and improve machine learning predictions involving arbitrary protein-small molecule interactions.

Genetic Diversity Analysis of Rhynocoris marginatus Fabricius Based on 18S Ribosomal RNA Gene (Heteroptera: Reduviidae)

The R. marginatus are essential components of ecosystem, but also important in the biological control of insect pest, infesting a varity of agro ecosystem and medicine. The present investigation was carried out in the insect molecular genetic variation of 18S Ribosomal RNA gene from R. marginatus. The study was represent by the reduviid insect R. marginatus nucleotide gene sequences were translate amino acid sequence and obtained hydropathy, Domain, Transmembranes of proteins were calculated. The multiple gene sequence alignment of in-silico translated amino acid sequence of the partial ribosomal genes protein of R. marginatus were generated and the phylogenetic relationships were observed.

Higher correlation between neutralizing antibodies and surrogate neutralizing or binding antibodies in COVID-19 patients than vaccine recipients.

This study determined the seropositive rates and levels of antibodies to severe acute respiratory syndrome coronavirus-2 in 50 patients and 108 vaccinees using microneutralization test (MNT), surrogate virus neutralization test (sVNT), chemiluminescent microparticle immunoassay (CMIA), and electrochemiluminescence immunoassay (ECLIA). MNT, as the reference method, employed living clade S and Delta viruses to measure neutralizing (NT) antibodies, while sVNT employed wild type strain and Delta receptor-binding domains (RBD) as the test antigens to measure sVNT antibodies. CMIA and ECLIA employed only one version of RBD to measure the binding antibodies. Our study performed S gene sequencing of the test virus to exclude undesired mutants that might lead to changes in antibody levels in MNT assay. We showed that spike protein amino acid sequences of our Delta virus contained 13 amino acid changes, with 3 related to the reduced neutralization. The MNT assay showed a significant reduction in seropositive rates and antibody levels in the patients' sera when the Delta variant replaced clade S as the test virus. In contrast, the seropositive rates determined by sVNT assay using wild type strain RBD and Delta RBD were non-significantly different, suggesting that sVNT assay could not identify the difference between the antigenicity of wild type RBD and Delta RBD. Furthermore, the correlation between the levels of NT and sVNT antibodies was moderate with the patients' sera but modest with the post-vaccination sera. The seropositive rates in the patients, as determined by CMIA or ECLIA, were not different from the MNT assay using clade S, but not Delta, as the test virus. In all analyses, the correlations between the antibody levels measured by MNT and the other 3 assays were modest to moderate, with the r-values of 0.3500-0.7882.

Strong evolutionary constraints against amino acid changes in the P2 subdomain of sapovirus GI.1 capsid protein VP1

Sapovirus (SaV) is a nonenveloped RNA virus that causes acute gastroenteritis in humans. Although SaV is a clinically important pathogen in children, an effective vaccine is currently unavailable. The capsid protein VP1 of SaVs forms the outer shell of the virion and is highly diverse, as often seen in the virion-surface proteins of RNA viruses, creating an obstacle for vaccine development. We here report a unique phenomenon pertaining to the variation of SaV VP1. Phylogenetic and information entropy analyses using full-length VP1 sequences from a public database consistently showed that the amino acid sequences of the VP1 protein have been highly conserved over more than 40 years in the major epidemic genotype GI.1 but not in GI.2. Structural modeling showed that even the VP1 P2 subdomain, which is arranged on the outermost shell of the virion and presumably exposed to anti-SaV antibodies, remained highly homogeneous in GI.1 but not in GI.2. These results suggest strong evolutionary constraints against amino acid changes in the P2 subdomain of the SaV GI.1 capsid and illustrate a hitherto unappreciated mechanism, i.e., preservation of the VP1 P2 subdomain, involved in SaV survival. Our findings could have important implications for the development of an anti-SaV vaccine.

Genomic context as well as sequence of both psr and penicillin-binding protein 5 contributes to β-lactam resistance in Enterococcus faecium.

The findings of this study shed light on ampicillin resistance in Enterococcus faecium clade A strains. They underscore the significance of alterations in the amino acid sequence of penicillin-binding protein 5 (PBP5) and the pivotal role of the psr region in PBP5 expression and ampicillin resistance. Notably, the presence of a full-length psrB leads to reduced PBP5 expression and lower minimum inhibitory concentrations (MICs) of ampicillin compared to the presence of a shorter psrA, regardless of the pbp5 allele involved. Additionally, clade B E. faecium strains exhibit lower AMP MICs when both psr alleles from clades A and B are present, although it is important to consider other distinctions between clade A and B strains that may contribute to this effect. It is intriguing to note that the divergence between clade A and clade B E. faecium and the subsequent evolution of heightened AMP MICs in hospital-associated strains appear to coincide with changes in Pbp5 and psr. These changes in psr may have resulted in an inactive Psr, facilitating increased PBP5 expression and greater ampicillin resistance. These results raise the possibility that a mimicker of PsrB, if one could be designed, might be able to lower MICs of ampicillin-resistant E. faecium, thus potentially resorting ampicillin to our therapeutic armamentarium for this species.

Deciphering the Virome of the Pimple-Shaped ‘Yali’ Pear Fruit through High-Throughput Sequencing

Viral diseases pose a threat to fruit tree growth. In this study, we observed some pimple-shaped ‘Yali’ pears (Pyrus bretschneideri Rehd.) and investigated their viral components. We used virome analysis to elucidate the viral composition within the pimple-shaped fruit. RT-PCR was applied to detect the plant viruses of fruits, leaves, and branches in ‘Yali’ pear. We also constructed a phylogenetic tree based on the amino acid sequences of the movement proteins of 6 apple stem grooving virus (ASGV) isolates and 44 ASGVs from the NCBI database. We detected ASGV and apple stem pitting virus (ASPV) in the pimple-shaped pear fruits, which is the first report of these viruses existing in ‘Yali’ pear fruits. ASGV was present in all pimple-shaped fruit samples from six ‘Yali’ pear-producing regions. The phylogenetic tree showed that ASGVs from pears, apples, and citrus plants were separated into different branches, suggesting that hosts influence the genetic diversity of ASGV. Our study revealed the viral components and genetic variation of ASGV in pimple-shaped pear fruit, providing new insights into the epidemiology of this virus.

Structural modeling and gene expression analysis of phosvitinless vitellogenin (vgc) in the Indian freshwater murrel, Channa punctatus (Bloch, 1793)

Vitellogenin (Vg) is a female-specific egg-yolk precursor protein, synthesized in the liver of fish in response to estrogens. In the present study, complete gene of phosvitinless vitellogenin (vgc) was sequenced, its 3D structure was predicted and validated by web-based softwares. The complete nucleotide sequence of vgc was 4126 bp which encodes for 1272 amino acids and showed the presence of three conserved domains viz. LPD_N, DUF1943 and DUF1944. The retrieved amino acid sequence of VgC protein was subjected to in silico analysis for understanding the structural and functional properties of protein. mRNA levels of multiple vg genes have also been quantified during annual reproductive cycle employing qPCR. A correlation has been observed between seasonal changes in gonadosomatic index with estradiol levels and hepatic expression of three types of vg genes (vga, vgb, vgc) during ovarian cycle of murrel. During preparatory phase, when photoperiod and temperature are low; low titre of E2 in blood induces expression of vgc gene. A rapid increase in the levels of E2 favours induction of vgb and vga genes in liver of murrel during early pre-spawning phase when photoperiod is long and temperature is high in nature. These results suggest that among three vitellogenin proteins, VgC is synthesized earlier than VgA and VgB during oogenesis.

Novel Aquareovirus isolated from channel catfish (Ictalurus punctatus) used in mussel restoration efforts in Wisconsin.

Channel catfish (Ictalurus punctatus) are a food fish extensively reared in aquaculture facilities throughout the world and are also among the most abundant wild catfish species in North America, making them a popular target of anglers. Furthermore, channel catfish are important members of aquatic ecosystems; for example, they serve as a glochidial host for the endangered winged mapleleaf mussel (Quadrula fragosa), making them critical for conserving this species through hatchery-based restoration efforts. During a routine health inspection, a novel aquareovirus was isolated from channel catfish used in mussel propagation efforts at a fish hatchery in Wisconsin. This virus was isolated on brown bullhead cells (ATCC CCL-59) and identified through metagenomic sequencing as a novel member of the family Spinareoviridae, genus Aquareovirus. The virus genome consists of 11 segments, as is typical of the aquareoviruses, with phylogenetic relationships based on RNA-dependent RNA polymerase and major outer capsid protein amino acid sequences showing it to be most closely related to golden shiner virus (aquareovirus C) and aquareovirus C/American grass carp reovirus (aquareovirus G) respectively. The potential of the new virus, which we name genictpun virus 1 (GNIPV-1), to cause disease in channel catfish or other species remains unknown.

Computational identification of antibody-binding epitopes from mimotope datasets.

Introduction: A fundamental challenge in computational vaccinology is that most B-cell epitopes are conformational and therefore hard to predict from sequence alone. Another significant challenge is that a great deal of the amino acid sequence of a viral surface protein might not in fact be antigenic. Thus, identifying the regions of a protein that are most promising for vaccine design based on the degree of surface exposure may not lead to a clinically relevant immune response. Methods: Linear peptides selected by phage display experiments that have high affinity to the monoclonal antibody of interest ("mimotopes") usually have similar physicochemical properties to the antigen epitope corresponding to that antibody. The sequences of these linear peptides can be used to find possible epitopes on the surface of the antigen structure or a homology model of the antigen in the absence of an antigen-antibody complex structure. Results and Discussion: Herein we describe two novel methods for mapping mimotopes to epitopes. The first is a novel algorithm named MimoTree that allows for gaps in the mimotopes and epitopes on the antigen. More specifically, a mimotope may have a gap that does not match to the epitope to allow it to adopt a conformation relevant for binding to an antibody, and residues may similarly be discontinuous in conformational epitopes. MimoTree is a fully automated epitope detection algorithm suitable for the identification of conformational as well as linear epitopes. The second is an ensemble approach, which combines the prediction results from MimoTree and two existing methods.

A pilot investigation of the association between HIV-1 Vpr amino acid sequence diversity and the tryptophan-kynurenine pathway as a potential mechanism for neurocognitive impairment

HIV infection compromises both the peripheral and central immune systems due to its pathogenic and neuropathogenic features. The mechanisms driving HIV-1 pathogenesis and neuropathogenesis involve a series of events, including metabolic dysregulation. Furthermore, HIV-subtype-specific variations, particularly alterations in the amino acid sequences of key viral proteins, are known to influence the severity of clinical outcomes in people living with HIV. However, the impact of amino acid sequence variations in specific viral proteins, such as Viral protein R (Vpr), on metabolites within the Tryptophan (Trp)-kynurenine (Kyn) pathway in people living with HIV remains unclear. Our research aimed to explore the relationship between variations in the Vpr amino acid sequence (specifically at positions 22, 41, 45, and 55, as these have been previously linked to neurocognitive function) and peripheral Trp-Kyn metabolites. Additionally, we sought to clarify the systems biology of Vpr sequence variation by examining the link between Trp-Kyn metabolism and peripheral inflammation, as a neuropathogenic mechanism. In this preliminary study, we analyzed a unique cohort of thirty-two (n = 32) South African cART naïve people living with HIV. We employed Sanger sequencing to ascertain blood-derived Vpr amino acid sequence variations and a targeted LC-MS/MS metabolomics platform to assess Trp-Kyn metabolites, such as Trp, Kyn, kynurenic acid (KA), and quinolinic acid (QUIN). Particle-enhanced turbidimetric assay and Enzyme-linked immunosorbent assays were used to measure immune markers, hsCRP, IL-6, suPAR, NGAL and sCD163. After applying Bonferroni corrections (p =.05/3) and adjusting for covariates (age and sex), only the Vpr G41 and A55 groups was nearing significance for higher levels of QUIN compared to the Vpr S41 and T55 groups, respectively (all p =.023). Multiple regression results revealed that Vpr amino acid variations at position 41 (adj R2 = 0.049, β = 0.505; p =.023), and 55 (adj R2 = 0.126, β = 0.444; p =.023) displayed significant associations with QUIN after adjusting for age and sex. Lastly, the higher QUIN levels observed in the Vpr G41 group were found to be correlated with suPAR (r =.588, p =.005). These results collectively underscore the importance of specific Vpr amino acid substitutions in influencing QUIN and inflammation (specifically suPAR levels), potentially contributing to our understanding of their roles in the pathogenesis and neuropathogenesis of HIV-1.

Milk and Its Derivatives as Sources of Components and Microorganisms with Health-Promoting Properties: Probiotics and Bioactive Peptides.

Milk is a source of many valuable nutrients, including minerals, vitamins and proteins, with an important role in adult health. Milk and dairy products naturally containing or with added probiotics have healthy functional food properties. Indeed, probiotic microorganisms, which beneficially affect the host by improving the intestinal microbial balance, are recognized to affect the immune response and other important biological functions. In addition to macronutrients and micronutrients, biologically active peptides (BPAs) have been identified within the amino acid sequences of native milk proteins; hydrolytic reactions, such as those catalyzed by digestive enzymes, result in their release. BPAs directly influence numerous biological pathways evoking behavioral, gastrointestinal, hormonal, immunological, neurological, and nutritional responses. The addition of BPAs to food products or application in drug development could improve consumer health and provide therapeutic strategies for the treatment or prevention of diseases. Herein, we review the scientific literature on probiotics, BPAs in milk and dairy products, with special attention to milk from minor species (buffalo, sheep, camel, yak, donkey, etc.); safety assessment will be also taken into consideration. Finally, recent advances in foodomics to unveil the probiotic role in human health and discover novel active peptide sequences will also be provided.

Effects of in ovo injection of bacterial peptides and CpG-ODN on Salmonella enterica serovar Heidelberg infection in specific pathogen-free (SPF) chicks

ABSTRACT The increasing prevalence of pathogenic and antimicrobial-resistant isolates of Salmonella Heidelberg (SH) represents a global concern. Consequently, novel strategies for preventing and controlling infections caused by this bacterium are needed. This study aimed to investigate the protective effects of in ovo injection with a formulation based on bacterial peptides, either alone or in combination with oligodeoxynucleotides containing cytosine-phosphodiester-guanine (CpG-ODN), against SH infection in chicks. Genomic data of SH available in public databases were analyzed to select amino acid sequences of structural proteins or those with greater relevance for intestinal colonization. These sequences were subjected to linear epitope prediction tools to identify highly immunogenic peptides. SPF eggs (n = 180) were incubated and injected via the allantoic cavity at day 18 of incubation. Results showed that in ovo injection of peptides + CpG-ODN reduced SH colonization in the caecal content during the first week post-infection, although it did not reduce overall faecal excretion throughout the study. Furthermore, CpG-ODN injection may positively affect intestinal health, as evidenced by reduced crypt depth at 21 dpi and increased villus height at 28 dpi. Levels of secretory IgA did not differ between chicks in any groups, and no detectable SH counts were found in the livers of chicks in any of the infected groups throughout the study. In conclusion, in ovo injection with peptides + CpG-ODN may help reduce caecal colonization by SH in the early stages of infection, but it does not impact total faecal excretion. Additionally, CpG-ODN injection may improve intestinal health parameters in chicks. RESEARCH HIGHLIGHTS Peptides + CpG-ODN reduced SH in caeca at the first week post-infection. Administered formulations did not reduce SH-faecal excretion. Levels of intestinal IgA were similar between all groups. CpG-ODN improved some parameters associated with chick intestinal health.

Enhancing Drug-Target Binding Affinity Prediction through Deep Learning and Protein Secondary Structure Integration

Background: Conventional approaches to drug discovery are often characterized by lengthy and costly processes. To expedite the discovery of new drugs, the integration of artificial intelligence (AI) in predicting drug-target binding affinity (DTA) has emerged as a crucial approach. Despite the proliferation of deep learning methods for DTA prediction, many of these methods primarily concentrate on the amino acid sequence of proteins. Yet, the interactions between drug compounds and targets occur within distinct segments within the protein structures, whereas the primary sequence primarily captures global protein features. Consequently, it falls short of fully elucidating the intricate relationship between drugs and their respective targets. Objective: This study aims to employ advanced deep-learning techniques to forecast DTA while incorporating information about the secondary structure of proteins. Methods: In our research, both the primary sequence of protein and the secondary structure of protein were leveraged for protein representation. While the primary sequence played the role of the overarching feature, the secondary structure was employed as the localized feature. Convolutional neural networks and graph neural networks were utilized to independently model the intricate features of target proteins and drug compounds. This approach enhanced our ability to capture drugtarget interactions more effectively Results: We have introduced a novel method for predicting DTA. In comparison to DeepDTA, our approach demonstrates significant enhancements, achieving a 3.9% increase in the Concordance Index (CI) and a remarkable 34% reduction in Mean Squared Error (MSE) when evaluated on the KIBA dataset. Conclusion: In conclusion, our results unequivocally demonstrate that augmenting DTA prediction with the inclusion of the protein's secondary structure as a localized feature yields significantly improved accuracy compared to relying solely on the primary structure.

First report of Kalanchoe Latent Virus naturally infecting common bean (Phaseolus vulgaris L.) in South Korea.

The common bean (Phaseolus vulgaris; family: Fabaceae) is an economically and nutritionally important food crop worldwide (Ganesan et al. 2017). In 2021, several plants collected from different provinces in South Korea had symptoms of viral infections (e.g., mild yellow-greenish speckling, stunting, crinkling, and deformed leaves). To identify the causal pathogens, total RNA was isolated from pooled leaf tissues from all samples (n = 29) for paired-end high-throughput sequencing (HTS). The cDNA library was constructed after eliminating ribosomal RNA using the TruSeq RNA Sample Prep Kit and then sequenced using the Illumina NovaSeq 6000 platform (Macrogen, Korea). The 297,868,156 paired-end clean reads (150 nt) were de novo assembled using Trinity with default parameters. BLASTx was used for the contig analysis, which revealed the pooled samples were infected with several plant viruses (e.g., turnip mosaic virus, zucchini yellow mosaic virus, cucumber mosaic virus, lily mottle virus). Notably, the assembled contigs included a single viral contig (8,472 nt) comprising the nearly complete KLV genome (HTS mean coverage: 39.46%). Kalanchoe latent virus (KLV; genus: Carlavirus; family: Betaflexiviridae) has been detected in Kalanchoë blossfeldiana (Hearon 1982), Chenopodium quinoa (Dinesen et al. 2009), and Graptopetalum paraguayense (Sorrentino et al. 2017). The sequence was most similar (96.28% nucleotide identity; 99% query coverage) to KLV isolate DSMZ PV-0290 (GenBank: OP525283) from Denmark. The contig sequence was validated via reverse transcription-polymerase chain reaction (RT-PCR) using total RNA extracted from the 29 individually stored samples and nine primer sets specific for the KLV contig. All nine contig-specific overlapping fragments were amplified from only a P. vulgaris plant with mild yellowing mosaic symptoms collected on July 6, 2021, in Jeongseon County, South Korea. Additionally, 5' and 3' rapid amplification of cDNA ends (RACE)-specific primers were designed for the KLV contig sequence to determine the terminal ends of the genome of the South Korean KLV isolate using the 5'/3' RACE System (Invitrogen, Carlsbad, CA, USA). All of the amplified and overlapping fragments were cloned into the RBC T&A Cloning Vector (RBC Bioscience, Taipei, Taiwan) and sequenced using the Sanger method. The obtained full-length genomic sequence of the KLV isolate (KLV-SK22) was 8,517 nt long and was deposited in GenBank OQ718816. According to the BLASTn analysis, KLV-SK22 was highly similar (96.30% sequence identity; 100% query coverage) to the DSMZ PV-0290 isolate. Phylogenetic trees constructed on the basis of coat protein and RNA-dependent RNA polymerase amino acid sequences revealed that KLV-SK22 is closely related to the DSMZ PV-0290 and PV-0290B isolates from Denmark, respectively. At the genome and gene levels, the individual sequence identities between the carlaviruses and other KLV isolates were 96.29% to 100% (Adams et al. 2004). Additionally, an RT-PCR analysis using detection primers specific for KLV-SK22 did not detect KLV in 15 samples (P. vulgaris = 3, Glycine max = 8, Pueraria montana = 2, Trifolium repens = 1, and Vigna angularis = 1) randomly collected from different regions in South Korea. Based on these results, KLV infection may not be widespread at this time in South Korea. To the best of our knowledge, this is the first report of KLV in P. vulgaris in South Korea or elsewhere. Our findings will aid future research on the epidemiology and long-term management of KLV-related diseases.

Identifying virulence factors using graph transformer autoencoder with ESMFold-predicted structures

With the increasing resistance of bacterial pathogens to conventional antibiotics, antivirulence strategies targeting virulence factors (VFs) have become an effective new therapy for the treatment of pathogenic bacterial infections. Therefore, the identification and prediction of VFs can provide ideal candidate targets for the implementation of antivirulence strategies in treating infections caused by pathogenic bacteria. Currently, the existing computational models predominantly rely on the amino acid sequences of virulence proteins while overlooking structural information. Here, we propose a novel graph transformer autoencoder for VF identification (GTAE-VF), which utilizes ESMFold-predicted 3D structures and converts the VF identification problem into a graph-level prediction task. In an encoder-decoder framework, GTAE-VF adaptively learns both local and global information by integrating a graph convolutional network and a transformer to implement all-pair message passing, which can better capture long-range correlations and potential relationships. Extensive experiments on an independent test dataset demonstrate that GTAE-VF achieves reliable and robust prediction accuracy with an AUC of 0.963, which is consistently better than that of other structure-based and sequence-based approaches. We believe that GTAE-VF has the potential to emerge as a valuable tool for assessing VFs and devising antivirulence strategies.

Transfer learning to leverage larger datasets for improved prediction of protein stability changes

Amino acid mutations that lower a protein's thermodynamic stability are implicated in numerous diseases, and engineered proteins with enhanced stability can be important in research and medicine. Computational methods for predicting how mutations perturb protein stability are, therefore, of great interest. Despite recent advancements in protein design using deep learning, in silico prediction of stability changes has remained challenging, in part due to a lack of large, high-quality training datasets for model development. Here, we describe ThermoMPNN, a deep neural network trained to predict stability changes for protein point mutations given an initial structure. In doing so, we demonstrate the utility of a recently released megascale stability dataset for training a robust stability model. We also employ transfer learning to leverage a second, larger dataset by using learned features extracted from ProteinMPNN, a deep neural network trained to predict a protein's amino acid sequence given its three-dimensional structure. We show that our method achieves state-of-the-art performance on established benchmark datasets using a lightweight model architecture that allows for rapid, scalable predictions. Finally, we make ThermoMPNN readily available as a tool for stability prediction and design.