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First computational design using lambda-superstrings and in vivo validation of SARS-CoV-2 vaccine
Luis Martínez1,2, Iker Malaina3,4, David Salcines-Cuevas5
1Department of Mathematics, Faculty of Science and Technology, University of the Basque Country, UPV/EHU, 48940, Leioa, Spain. luis.martinez@ehu.eus.
Abstract:
Coronavirus disease 2019 (COVID-19) is the greatest threat to global health at the present time, and considerable public and private effort is being devoted to fighting this recently emerged disease. Despite the undoubted advances in the development of vaccines against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of COVID-19, uncertainty remains about their future efficacy and the duration of the immunity induced. It is therefore prudent to continue designing and testing vaccines against this pathogen. In this article we computationally designed two candidate vaccines, one monopeptide and one multipeptide, using a technique involving optimizing lambda-superstrings, which was introduced and developed by our research group. We tested the monopeptide vaccine, thus establishing a proof of concept for the validity of the technique. We synthesized a peptide of 22 amino acids in length, corresponding to one of the candidate vaccines, and prepared a dendritic cell (DC) vaccine vector loaded with the 22 amino acids SARS-CoV-2 peptide (positions 50-71) contained in the NTD domain (DC-CoVPSA) of the Spike protein. Next, we tested the immunogenicity, the type of immune response elicited, and the cytokine profile induced by the vaccine, using a non-related bacterial peptide as negative control. Our results indicated that the CoVPSA peptide of the Spike protein elicits noticeable immunogenicity in vivo using a DC vaccine vector and remarkable cellular and humoral immune responses. This DC vaccine vector loaded with the NTD peptide of the Spike protein elicited a predominant Th1-Th17 cytokine profile, indicative of an effective anti-viral response. Finally, we performed a proof of concept experiment in humans that included the following groups: asymptomatic non-active COVID-19 patients, vaccinated volunteers, and control donors that tested negative for SARS-CoV-2. The positive control was the current receptor binding domain epitope of COVID-19 RNA-vaccines. We successfully developed a vaccine candidate technique involving optimizing lambda-superstrings and provided proof of concept in human subjects. We conclude that it is a valid method to decipher the best epitopes of the Spike protein of SARS-CoV-2 to prepare peptide-based vaccines for different vector platforms, including DC vaccines.
Insights
Researchers developed a novel peptide vaccine candidate for COVID-19 using lambda-superstring optimization. This dendritic cell vaccine, targeting the SARS-CoV-2 Spike protein, demonstrated significant immunogenicity and induced a protective Th1-Th17 cytokine profile in human trials.
Area of Science:
- Immunology
- Vaccinology
- Computational Biology
Background:
- Coronavirus disease 2019 (COVID-19) remains a global health threat, necessitating continuous vaccine development despite existing advancements.
- Uncertainty regarding the long-term efficacy and duration of immunity from current severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccines underscores the need for alternative vaccine strategies.
- Peptide-based vaccines offer a promising avenue for developing new vaccines against SARS-CoV-2.
Purpose of the Study:
- To computationally design novel peptide vaccine candidates against SARS-CoV-2 using lambda-superstring optimization.
- To evaluate the immunogenicity, immune response type, and cytokine profile of a dendritic cell (DC) vaccine vector loaded with a SARS-CoV-2 Spike protein peptide (DC-CoVPSA).
- To establish proof of concept for the lambda-superstring optimization technique and the DC-CoVPSA vaccine in human subjects.
Main Methods:
- Computational design of monopeptide and multipeptide vaccine candidates using lambda-superstring optimization.
- Synthesis of a 22-amino acid peptide (CoVPSA) from the SARS-CoV-2 Spike protein NTD domain and its loading onto a DC vaccine vector.
- In vivo immunogenicity testing in animal models and ex vivo analysis of immune responses and cytokine profiles (Th1-Th17).
- Proof of concept human trials involving asymptomatic COVID-19 patients, vaccinated volunteers, and control donors, with current RNA-vaccine epitopes as positive control.
Main Results:
- The CoVPSA peptide demonstrated significant immunogenicity in vivo when delivered via a DC vaccine vector.
- The DC-CoVPSA vaccine elicited robust cellular and humoral immune responses, characterized by a predominant Th1-Th17 cytokine profile indicative of effective antiviral activity.
- The lambda-superstring optimization technique was validated, and proof of concept was successfully established in human subjects.
Conclusions:
- The lambda-superstring optimization technique is a valid and effective method for identifying optimal epitopes from the SARS-CoV-2 Spike protein for peptide-based vaccine development.
- The developed DC vaccine vector (DC-CoVPSA) represents a promising candidate for a peptide-based vaccine against COVID-19.
- This approach is adaptable for various vector platforms, offering flexibility in future vaccine design against SARS-CoV-2.
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