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Updated: Jun 16, 2025
JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
Published on: October 19, 2021
Integrated structural proteomics and machine learning-guided mapping of a highly protective precision vaccine against
Abbas Khan1, Muhammad Ammar Zahid1, Farheen Farrukh2
1Department of Pharmaceutical Sciences, College of Pharmacy, QU Health, Qatar University, P.O. Box 2713, Doha, Qatar.
Abstract:
Mycoplasma pulmonis (M. pulmonis) is an emerging respiratory infection commonly linked to prostate cancer, and it is classified under the group of mycoplasmas. Improved management of mycoplasma infections is essential due to the frequent ineffectiveness of current antibiotic treatments in completely eliminating these pathogens from the host. The objective of this study is to design and construct effective and protective vaccines guided by structural proteomics and machine learning algorithms to provide protection against the M. pulmonis infection. Through a thorough examination of the entire proteome of M. pulmonis, four specific targets Membrane protein P80, Lipoprotein, Uncharacterized protein and GGDEF domain-containing protein have been identified as appropriate for designing a vaccine. The proteins underwent mapping of cytotoxic T lymphocyte (CTL), helper T lymphocyte (HTL) (IFN)-γ ±, and B-cell epitopes using artificial and recurrent neural networks. The design involved the creation of mRNA and peptide-based vaccine, which consisted of 8 CTL epitopes associated by GGS linkers, 7 HTL (IFN-positive) epitopes, and 8 B-cell epitopes joined by GPGPG linkers. The vaccine designed exhibit antigenic behavior, non-allergenic qualities, and exceptional physicochemical attributes. Structural modeling revealed that correct folding is crucial for optimal functioning. The coupling of the MEVC and Toll-like Receptors (TLR)1, TLR2, and TLR6 was examined through molecular docking experiments. This was followed by molecular simulation investigations, which included binding free energy estimations. The results indicated that the dynamics of the interaction were stable, and the binding was strong. In silico cloning and optimization analysis revealed an optimized sequence with a GC content of 49.776 % and a CAI of 0.982. The immunological simulation results showed strong immune responses, with elevated levels of active and plasma B-cells, regulatory T-cells, HTL, and CTL in both IgM+IgG and secondary immune responses. The antigen was completely cleared by the 50th day. This study lays the foundation for creating a potent and secure vaccine candidate to combat the newly identified M. pulmonis infection in people.
Insights
This study developed a novel vaccine against Mycoplasma pulmonis, an emerging respiratory pathogen. Computational methods identified key proteins and epitopes, leading to a designed mRNA and peptide vaccine that shows strong simulated immune responses and pathogen clearance.
Area of Science:
- Infectious Diseases
- Vaccinology
- Computational Biology
Background:
- Mycoplasma pulmonis is an emerging respiratory pathogen linked to prostate cancer.
- Current antibiotic treatments are often ineffective for complete pathogen elimination.
- Development of effective vaccines is crucial for managing M. pulmonis infections.
Purpose of the Study:
- To design and construct a protective vaccine against M. pulmonis using structural proteomics and machine learning.
- To identify potential vaccine targets within the M. pulmonis proteome.
- To evaluate the vaccine's immunogenicity and efficacy through in silico methods.
Main Methods:
- Proteomic analysis of M. pulmonis to identify vaccine target proteins.
- Machine learning algorithms (artificial and recurrent neural networks) for epitope mapping (CTL, HTL, B-cell).
- In silico vaccine design (mRNA and peptide-based), molecular docking, simulations, and immunological modeling.
Main Results:
- Four target proteins (Membrane protein P80, Lipoprotein, Uncharacterized protein, GGDEF domain-containing protein) were identified.
- Designed vaccine constructs showed antigenic, non-allergenic properties with favorable physicochemical attributes.
- Molecular docking and simulations confirmed stable and strong binding interactions.
- Immunological simulations predicted robust immune responses and complete antigen clearance by day 50.
Conclusions:
- A potent and secure vaccine candidate against M. pulmonis has been designed using a multi-computational approach.
- The study provides a foundation for developing a novel vaccine for human use against M. pulmonis.
- In silico findings suggest high potential for in vivo efficacy and safety.
