The ClusPro AbEMap web server for the prediction of antibody epitopes

Design of multi-epitope chimeric phage nanocarrier vaccines for porcine deltacoronavirus

Porcine delta coronavirus (PDCoV) poses a significant threat to the swine industry. Thus, the development of innovative vaccine candidates is critical for PDCoV prevention. This study details the creation of a PDCoV nanoparticle vaccine utilizing bacteriophage (phage) T4 as a delivery platform. B cell and T cell epitopes of the PDCoV spike (S) protein were identified through bioinformatics and assembled into a tandem construct (termed Pep) using a linker. In silico molecular docking revealed stable interactions between Pep and TLR3. Immune stimulation predictions indicated that Pep could trigger a robust immune response. The prokaryotic Pep protein was conjugated with T4 phage to generate the recombinant T4-Pep phage. Experimental data demonstrated that a single T4 phage displayed at least 830 copies of Pep. In a mouse immunoprotection assay, T4-Pep induced significantly higher levels of specific IgG antibodies and superior neutralizing antibody titers against PDCoV compared to the Pep naked peptide antigen. Moreover, T4 phage exhibited potent immunostimulatory effects, with immunized mice showing protection against PDCoV infection. Histological analysis revealed enhanced intestinal mucosal integrity post-immunization. These findings suggest that bacteriophages are promising vectors for the efficient delivery of viral epitopes, offering a potential platform for developing vaccines against porcine enteric coronaviruses.

Interaction between bacterial phytochromes Agp1 and Agp2 of Agrobacterium fabrum by fluorescence resonance energy transfer and docking studies

Phytochromes are biliprotein photoreceptors found in bacteria, fungi, and plants. The soil bacterium Agrobacterium fabrum has two phytochromes, Agp1 and Agp2, which work together to control DNA transfer to plants and bacterial conjugation. Both phytochromes interact as homodimeric proteins. For fluorescence resonance energy transfer (FRET) measurements, various Agp1 mutants and wild-type Agp2 were labeled with specific fluorophores to study their interaction. FRET efficiencies rose from position 122 to 545 of Agp1. The photosensory chromophore module (PCM) of Agp1 did not show a FRET signal, but the PCM of Agp2 did. Docking models suggest that Agp1 and Agp2 interact with their histidine kinase and PCM perpendicular to each, around 45 amino acids of Agp1 or Agp2 are involved.

Dockground: The resource expands to protein-RNA interactome

RNA is a master regulator of cellular processes and will bind to many different proteins throughout its life cycle. Dysregulation of RNA and RNA-binding proteins can lead to various diseases, including cancer. To better understand molecular mechanisms of the cellular processes, it is important to characterize protein-RNA interactions at the structural level. There is a lack of experimental structures available for protein-RNA complexes due to the RNA inherent flexibility, which complicates the experimental structure determination. The scarcity of structures can be made up for with computational modeling. Dockground is a resource for development and benchmarking of structure-based modeling of protein interactions. It contains datasets focusing on different aspects of protein recognition. The foundation of all the datasets is the database of experimentally determined protein complexes, which previously contained only protein-protein assemblies. To further expand the utility of the Dockground resource, we extended the database to protein-RNA interactions. The new functionalities are available on the Dockground website at https://dockground.compbio.ku.edu/. The database can be searched using a number of criteria, including removal of redundancies at various sequence and structure similarity thresholds. The database updates with new structures from the Protein Data Bank on a weekly basis.

PAbFold: Linear Antibody Epitope Prediction using AlphaFold2

Defining the binding epitopes of antibodies is essential for understanding how they bind to their antigens and perform their molecular functions. However, while determining linear epitopes of monoclonal antibodies can be accomplished utilizing well-established empirical procedures, these approaches are generally labor- and time-intensive and costly. To take advantage of the recent advances in protein structure prediction algorithms available to the scientific community, we developed a calculation pipeline based on the localColabFold implementation of AlphaFold2 that can predict linear antibody epitopes by predicting the structure of the complex between antibody heavy and light chains and target peptide sequences derived from antigens. We found that this AlphaFold2 pipeline, which we call PAbFold, was able to accurately flag known epitope sequences for several well-known antibody targets (HA / Myc) when the target sequence was broken into small overlapping linear peptides and antibody complementarity determining regions (CDRs) were grafted onto several different antibody framework regions in the single-chain antibody fragment (scFv) format. To determine if this pipeline was able to identify the epitope of a novel antibody with no structural information publicly available, we determined the epitope of a novel anti-SARS-CoV-2 nucleocapsid targeted antibody using our method and then experimentally validated our computational results using peptide competition ELISA assays. These results indicate that the AlphaFold2-based PAbFold pipeline we developed is capable of accurately identifying linear antibody epitopes in a short time using just antibody and target protein sequences. This emergent capability of the method is sensitive to methodological details such as peptide length, AlphaFold2 neural network versions, and multiple-sequence alignment database. PAbFold is available at https://github.com/jbderoo/PAbFold.

The evolutionary features and roles of single nucleotide variants and charged amino acid mutations in influenza outbreaks during NPI period

The epidemic and outbreaks of influenza B Victoria lineage (Bv) during 2019-2022 led to an analysis of genetic, epitopes, charged amino acids and Bv outbreaks. Based on the National Influenza Surveillance Network (NISN), the Bv 72 strains isolated during 2019-2022 were selected by spatio-temporal sampling, then were sequenced. Using the Compare Means, Correlate and Cluster, the outbreak data were analyzed, including the single nucleotide variant (SNV), amino acid (AA), epitope, evolutionary rate (ER), Shannon entropy value (SV), charged amino acid and outbreak. With the emergence of COVID-19, the non-pharmaceutical interventions (NPIs) made Less distant transmission and only Bv outbreak. The 2021-2022 strains in the HA genes were located in the same subset, but were distinct from the 2019-2020 strains (P < 0.001). The codon G → A transition in nucleotide was in the highest ratio but the transversion of C → A and T → A made the most significant contribution to the outbreaks, while the increase in amino acid mutations characterized by polar, acidic and basic signatures played a key role in the Bv epidemic in 2021-2022. Both ER and SV were positively correlated in HA genes (R = 0.690) and NA genes (R = 0.711), respectively, however, the number of mutations in the HA genes was 1.59 times higher than that of the NA gene (2.15/1.36) from the beginning of 2020 to 2022. The positively selective sites 174, 199, 214 and 563 in HA genes and the sites 73 and 384 in NA genes were evolutionarily selected in the 2021-2022 influenza outbreaks. Overall, the prevalent factors related to 2021-2022 influenza outbreaks included epidemic timing, Tv, Ts, Tv/Ts, P137 (B → P), P148 (B → P), P199 (P → A), P212 (P → A), P214 (H → P) and P563 (B → P). The preference of amino acid mutations for charge/pH could influence the epidemic/outbreak trends of infectious diseases. Here was a good model of the evolution of infectious disease pathogens. This study, on account of further exploration of virology, genetics, bioinformatics and outbreak information, might facilitate further understanding of their deep interaction mechanisms in the spread of infectious diseases.

AlphaFold2 in biomedical research: facilitating the development of diagnostic strategies for disease

Proteins, as the primary executors of physiological activity, serve as a key factor in disease diagnosis and treatment. Research into their structures, functions, and interactions is essential to better understand disease mechanisms and potential therapies. DeepMind's AlphaFold2, a deep-learning protein structure prediction model, has proven to be remarkably accurate, and it is widely employed in various aspects of diagnostic research, such as the study of disease biomarkers, microorganism pathogenicity, antigen-antibody structures, and missense mutations. Thus, AlphaFold2 serves as an exceptional tool to bridge fundamental protein research with breakthroughs in disease diagnosis, developments in diagnostic strategies, and the design of novel therapeutic approaches and enhancements in precision medicine. This review outlines the architecture, highlights, and limitations of AlphaFold2, placing particular emphasis on its applications within diagnostic research grounded in disciplines such as immunology, biochemistry, molecular biology, and microbiology.

ATG5 attenuates inflammatory signaling in mouse embryonic stem cells to control differentiation

Attenuated inflammatory response is a property of embryonic stem cells (ESCs). However, the underlying mechanisms are unclear. Moreover, whether the attenuated inflammatory status is involved in ESC differentiation is also unknown. Here, we found that autophagy-related protein ATG5 is essential for both attenuated inflammatory response and differentiation of mouse ESCs and that attenuation of inflammatory signaling is required for mouse ESC differentiation. Mechanistically, ATG5 recruits FBXW7 to promote ubiquitination and proteasome-mediated degradation of β-TrCP1, resulting in the inhibition of nuclear factor κB (NF-κB) signaling and inflammatory response. Moreover, differentiation defects observed in ATG5-depleted mouse ESCs are due to β-TrCP1 accumulation and hyperactivation of NF-κB signaling, as loss of β-TrCP1 and inhibition of NF-κB signaling rescued the differentiation defects. Therefore, this study reveals a previously uncharacterized mechanism maintaining the attenuated inflammatory response in mouse ESCs and further expands the understanding of the biological roles of ATG5.

Peptide Antibodies: Current Status

Peptide antibodies have become one of the most important classes of reagents in molecular biology and clinical diagnostics. For this reason, methods for their production and characterization continue to be developed, including basic peptide synthesis protocols, peptide-conjugate production and characterization, conformationally restricted peptides, immunization procedures, etc. Detailed mapping of peptide antibody epitopes has yielded important information on antibody-antigen interaction in general and specifically in relation to antibody cross-reactivity and theories of molecular mimicry. This information is essential for detailed understanding of paratope-epitope dynamics, design of antibodies for research, design of peptide-based vaccines, development of therapeutic peptide antibodies, and de novo design of antibodies with predetermined specificity.