A positively charged surface patch on the pestivirus NS3 protease module plays an important role in modulating NS3 helicase activity and virus production

Identification of three novel B cell epitopes targeting the bovine viral diarrhea virus NS3 protein for use in diagnostics and vaccine development

Bovine viral diarrhea virus (BVDV) is a major pathogen in cattle herds, widely distributed across the globe and causing significant economic losses to the cattle industry. The nonstructural protein NS3 is highly conserved across BVDV subtypes. Identifying and screening epitopes on BVDV NS3 is crucial for developing sensitive, specific diagnostic tools. In this study, we obtained three monoclonal antibodies (mAbs) against the NS3 protein: 2F7, 3E8, and 4D6. Three novel linear B-cell epitope 100EYG102, 384FLDIA388, and 100EYGVK104 were identified through reactions of these mAbs with a series of continuous-truncated peptides and one of which a rare three-amino-acid B-cell epitope 100EYG102. Critical amino acid residues were further characterized through alanine (A)-scanning mutagenesis. Sequence alignment revealed that 100EYG102 and 100EYGVK104 were highly conserved allowing mAbs 2F7 and 4D6 to recognize all BVDV subtypes. In contrast, 384FLDIA388 was specifically conserved in BVDV-1 and BVDV-3 enabling 3E8 mAb to differential diagnosis BVDV-2 from other BVDV subtypes. Additionally, preliminary diagnostic assays for BVDV were established by western blotting and peptide-based blocking ELISA. Moreover, we observed that these mAbs could inhibit the replication of BVDV. These findings provide a theoretical foundation for developing of therapeutic strategies for nonstructural protein and accurate diagnostic procedures.

Pred-AHCP: Robust Feature Selection-Enabled Sequence-Specific Prediction of Anti-Hepatitis C Peptides via Machine Learning

Every year, an estimated 1.5 million people worldwide contract Hepatitis C, a significant contributor to liver problems. Although many studies have explored machine learning's potential to predict antiviral peptides, very few have addressed the problem of predicting peptides against specific viruses such as Hepatitis C. In this study, we demonstrate the application and fine-tuning of machine learning (ML) algorithms to predict peptides that are effective against Hepatitis C virus (HCV). We developed a fine-tuned and explainable ML model that harnesses the amino acid sequence of a peptide to predict its anti-hepatitis C potential. Specifically, features were computed based on sequence and physicochemical properties. The feature selection was performed using a combined strategy of mutual information and variance inflation factor. This facilitated the removal of redundant and multicollinear features, enhancing the model's generalizability in predicting anti-hepatitis C peptides (AHCPs). The model using the random forest algorithm produced the best performance with an accuracy of about 92%. The feature analysis highlights that the distributions of hydrophobicity, polarizability, coil-forming residues, frequency of glycine residues and the existence of dipeptide motifs VL, LV, and CC emerged as the key predictors for identifying AHCPs targeting different components of HCV. The developed model can be accessed through the Pred-AHCP web server, provided at http://tinyurl.com/web-Pred-AHCP. This resource facilitates the prediction and re-engineering of AHCPs for designing peptide-based therapeutics while also proposing an exploration of similar strategies for designing peptide inhibitors effective against other viruses. The developed ML model can also be used for validating peptide sequences generated using generative artificial intelligence methods for further optimization.

Safety and Immunogenicity of Chimeric Pestivirus KD26_E2LOM in Piglets and Calves

A chimeric pestivirus (KD26_E2LOM) was prepared by inserting the E2 gene of the classical swine fever virus (CSFV) LOM strain into the backbone of the bovine viral diarrhea virus (BVDV) KD26 strain. KD26_E2LOM was obtained by transfecting the cDNA pACKD26_E2LOM into PK-15 cells. KD26_E2LOM chimeric pestivirus proliferated to titers of 106.5 TCID50/mL and 108.0 TCID50/mL at 96 h post-inoculation into PK-15 cells or MDBK cells, respectively. It also reacted with antibodies specific for CSFV E2 and BVDV Erns, but not with an anti-BVDV E2 antibody. Piglets (55-60 days old) inoculated with a high dose (107.0 TCID50/mL) of KD26_E2LOM produced high levels of CSFV E2 antibodies. In addition, no co-habiting pigs were infected with KD26_E2LOM; however, some inoculated pigs excreted the virus, and the virus was detected in some organs. When pregnant sows were inoculated during the first trimester (55-60 days) with a high dose (107.0 TCID50/mL) of KD26_E2LOM, anti-CSFV E2 antibodies were produced at high levels; chimeric pestivirus was detected in one fetus and in the ileum of one sow. When 5-day-old calves that did not consume colostrum received a high dose (107.0 TCID50/mL) of KD26_E2LOM, one calf secreted the virus in both feces and nasal fluid on Day 2. A high dose of KD26_E2LOM does not induce specific clinical signs in most animals, does not spread from animal to animal, and generates CSFV E2 antibodies with DVIA functions. Therefore, chimeric pestivirus KD26_E2LOM is a potential CSFV live marker vaccine.