Advances in Malaria Vaccine Development Targeting Plasmodium falciparum Surface Proteins
Fatoumata Gnine FOFANA
African Center of Excellence in Bioinformatics and Data Science (ACE-B), University of Sciences, Techniques and Technologies of Bamako (USTTB), Bamako, Mali.
Mamadou WELE *
African Center of Excellence in Bioinformatics and Data Science (ACE-B), University of Sciences, Techniques and Technologies of Bamako (USTTB), Bamako, Mali.
Oudou DIABATE
African Center of Excellence in Bioinformatics and Data Science (ACE-B), University of Sciences, Techniques and Technologies of Bamako (USTTB), Bamako, Mali.
Cheickna CISSE
African Center of Excellence in Bioinformatics and Data Science (ACE-B), University of Sciences, Techniques and Technologies of Bamako (USTTB), Bamako, Mali.
Abdoulaye DIAWARA
African Center of Excellence in Bioinformatics and Data Science (ACE-B), University of Sciences, Techniques and Technologies of Bamako (USTTB), Bamako, Mali.
Mahamadoun Haram TOURE
African Center of Excellence in Bioinformatics and Data Science (ACE-B), University of Sciences, Techniques and Technologies of Bamako (USTTB), Bamako, Mali.
Mahamadou DIAKITE
University of Sciences, Techniques and Technologies of Bamako (USTTB), Bamako, Mali.
Seydou O. DOUMBIA
University Clinical Research Centre (UCRC), University of Sciences, Techniques and Technologies of Bamako (USTTB), Bamako, Mali.
Abdoulaye DJIMDE
University of Sciences, Techniques and Technologies of Bamako (USTTB), Bamako, Mali.
*Author to whom correspondence should be addressed.
Abstract
Malaria remains a significant public health concern, especially in endemic regions where increasing drug resistance poses challenges to effective treatment. Vaccine development has emerged as a crucial strategy for malaria control, targeting different stages of the parasite’s lifecycle, including pre-erythrocytic, blood-stage, and transmission-blocking vaccines. Among these, merozoite surface proteins (MSPs) erythrocyte-binding antigens (EBAs), circumsporozoïte protein (CSP) and placental malaria vaccine candidates (VAR2CSA) have been extensively studied for their roles in parasite invasion and immune evasion. Despite advances in vaccine formulation, challenges such as antigenic variation, immune response variability and logistical constraints continue to limit widespread implementation.
Computational approaches including Bioinformatics, Chemoinformatics and Immunoinformatics, have transformed vaccine research by improving antigen identification, immune response prediction and vaccine design. Machine learning (ML) and artificial intelligence (AI) further enhance these processes enabling the development of mRNA-based vaccines, self-assembling protein nanoparticles (SAPN), and multi-antigen strategies. While vaccines such as RTS,S/AS01 and R21/Matrix-M have demonstrated partial efficacy, new-generation formulations incorporating multi-omics and AI-driven models are being explored to improve immunogenicity and durability. Despite these advancements, achieving long-term immunity and broad protection against diverse parasite strains remains a challenge.
Further research is required to address antigen stability, optimize delivery systems, and overcome barriers to vaccine accessibility in endemic regions. Strengthening global collaboration, investment in research, and large-scale clinical trials will be critical for developing a highly effective malaria vaccine.
The goal of this review is to analyze recent advances in malaria vaccine development targeting Plasmodium falciparum surface proteins, with a focus on computational approaches.
Keywords: Plasmodium falciparum, vaccine development, computational vaccinology, antigenic variation, polymorphism