Eclet. Quim. 50 | e-1555, 2025 https://doi.org/10.26850/1678-4618.eq.v50.2025.e1555 ISSN 1678-4618 page 1/13 1Helix Biogen Institute, Division of Vaccine Design and Development, Ogbomosho, Nigeria. 2Ladoke Akintola University of Technology, Department of Medical Microbiology and Parasitology, Ogbomosho, Nigeria. 3Adeleke University, Laboratory of Molecular Biology, Immunology and Bioinformatics, Ede, Nigeria. 4Babcock University Teaching Hospital, Molecular and Tissue culture Laboratory, Ilishan-Remo, Nigeria. 5Ambrose Alli University, Department of Biochemistry, Ekpoma, Nigeria. 6Precious Cornerstone University, Department of Natural Science, Ibadan, Nigeria. 7Department of Animal Science, Niger Delta University, Wilberforce Island, Bayelsa. 8Clemson University, Department of Microbiology, Clemson, United States. 9University of Colombo, Institute of Biochemistry, Molecular Biology and Biotechnology, Colombo, Sri Lanka. 10University of Ilorin, Department of Forest Resources Management, Ilorin, Nigeria. 11Ladoke Akintola University of Technology, Department of Community/Public Health Nursing, Ogbomosho, Nigeria. +Corresponding author: James Akinwumi Ogunniran, Phone: +2348031315189, Email address: ogunniranjames2017@gmail.com Original Article Immunoinformatics designing of peptide-based vaccine for malaria infection James Akinwumi Ogunniran1,2+ , Elijah Kolawole Oladipo1,3 , Kemiki Olalekan Ademola4 , Anthony Godswill Imolele5 , Olaoluwa Kehinde Alao6 , Kehinde Oluyemi Ajayi6 , Michael Asebake Ockiya7 , Oluseyi Rotimi Taiwo8 , Caleb Enejoh Omede9 , Samuel Nzube Nwosu10 , Adeola Christianah Ogunwole11 Abstract Malaria, a life-threatening disease prevalent in tropical regions, primarily affects infants, children under five, pregnant women, travelers, and individuals with HIV/AIDS. This study utilized an immunoinformatics approach to design a peptide-based malaria vaccine targeting antigenic proteins, including Apical Membrane Antigen 1, Knob-Associated Histidine-Rich Protein, Merozoite Surface Protein 1, and Sporozoite Surface Protein 2. Antigenic protein sequences were screened for antigenicity, allergenicity, toxicity, and immune responses involving CTLs, B-cells, and HTLs. Selected epitopes were linked with appropriate linkers and an adjuvant to enhance immunogenicity, forming a vaccine construct. The construction, comprising 1473 amino acids, exhibited a molecular weight of 15.21 kDa, a theoretical pI of 8.94, an aliphatic index of 60.01, and an instability index of 31.66, indicating stability. It was hydrophilic (GRAVY: –0.385) with favorable half-lives in mammalian, yeast, and E. coli systems. Docking studies showed strong binding affinity to human TLR2 and TLR4. In silico cloning indicated a CAI value of 0.92 and a GC content of 59.31%. Further studies are needed to validate its efficacy and safety. Article History Received April 22, 2024 Accepted December 05, 2024 Published May 25, 2025 Keywords 1. malaria vaccine; 2.immunoinformatics; 3.antigenic proteins; 4.epitope prediction; 5.molecular docking. Section Editors Marcos Carlos de Mattos Highlights Malaria is caused by Plasmodium parasites, transmitted by Anopheles mosquitoes. It is lethal and affects vulnerable populations like children and pregnant women. Current vaccines, like RTS, S, offer limited protection. New vaccine approaches focus on multi-epitope designs to enhance immune responses. Vaccines incorporate CTL, HTL, B-cell epitopes, enhancing protection against malaria. https://doi.org/10.26850/1678-4618.eq.v50.2025.e1555 https://ror.org/043hyzt56 https://ror.org/03gnb6c23 https://ror.org/006pw7k84 https://ror.org/04fzaxn66 https://ror.org/04fzaxn66 https://ror.org/037s24f05 https://ror.org/02phn5242 https://ror.org/032kdwk38 https://ror.org/032kdwk38 https://ror.org/043hyzt56 mailto:ogunniranjames2017@gmail.com mailto:ogunniranjames2017@gmail.com mailto:koladipo2k3@yahoo.co.uk mailto:kemiki.olalekan@gmail.com mailto:anthonyimolele@gmail.com mailto:alaoolaoluwa@outlook.com mailto:ajayikehinde@pcu.edu.ng mailto:mikeockiya@gmail.com mailto:oluseyitaiwo7@gmail.com mailto:omedecaleb.e@gmail.com mailto:nwosunzube71@gmail.com mailto:ogunwole31@gmail.com https://orcid.org/0009-0001-6931-5946 https://orcid.org/0000-0002-9646-5122 https://orcid.org/0000-0001-6837-9326 https://orcid.org/0000-0002-3093-3309 https://orcid.org/0000-0003-1647-879X https://orcid.org/0000-0002-2562-8748 https://orcid.org/0000-0002-6799-2679 https://orcid.org/0000-0002-9475-9987 https://orcid.org/0009-0003-5520-9573 https://orcid.org/0009-0009-1877-848X https://orcid.org/0009-0006-1085-8280 Original Article https://doi.org/10.26850/1678-4618.eq.v50.2025.e1555 ISSN 1678-4618 page 2/13 1. Introduction Malaria is regarded as one of the most lethal and incapacitating infectious diseases, marked by intermittent bouts of high fever. Its cerebral form is particularly concerning, as it can lead to severe neurological complications such as brain damage and coma (Pandey et al., 2017). The global incidence of malaria cases has been alarmingly high, reaching 247 million in 2021, 245 million in 2020, and 232 million in 2019 (WHO, 2022). Specific populations are particularly vulnerable to malaria infection, including children under the age of 5, HIV-positive individuals, pregnant women (Schumacher et al., 2012), and travelers (Chaves et al., 2017). The protozoan parasite Plasmodium is the causative agent of malaria, transmitted to humans through the bite of previously infected female Anopheles mosquitoes (Biamonte et al., 2013). Among humans, malaria infection is caused by several species of the Plasmodium genus, including P. falciparum, P. ovale, P. malariae, and P. vivax (Amir et al., 2022). A newly identified species, P. knowlesi, has been recognized in Southeast Asian countries, particularly Malaysia, as the sixth species causing malaria infection (Singh et al., 2004). P. vivax is commonly associated with the sub-Saharan region of Africa and contributes significantly to morbidity, while P. falciparum, the most severe type of parasite, is prevalent in Africa and is responsible for most malaria-related deaths (Amir et al., 2022). The malaria parasite's life cycle consists of four phases: the liver stage, blood stage, human transmission stage, and mosquito stage. Each stage must be considered for effective therapy to achieve complete eradication of the disease (Crompton et al., 2010). After being released from the mosquito bite, sporozoites migrate into the bloodstream from the bite site and invade hepatocytes in the liver (Lindner et al., 2012). In the liver stage, the parasites undergo proliferation for approximately seven days (a week) before releasing exo-erythrocytic merozoites into the bloodstream to initiate the blood stage of infection (Sinnis et al., 2008). Recurrent cycles of replication during the blood stage of infection lead to an exponential increase in the number of malaria parasites and the manifestation of all clinical symptoms associated with malaria (Kristian et al., 2016). Before the initiation of the symptomatic stage of the disease, the parasite can be eliminated by targeting the asymptomatic sporozoite and liver stage parasites when parasite populations are low (Kristian et al., 2016). Additionally, peptides or antibodies that block the AMA1-RON2 connection reduced the Plasmodium merozoites' capacity to colonize the host cells (Srinivasan et al., 2011). The protein known as AHRP, or knob-associated histidine-rich protein (KAHRP) is usually produced when Plasmodium falciparum initiates infection in the erythrocytes (Maier et al., 2009). A glycosylphosphatidylinositol-anchored protein known as merozoite surface protein 1 (MSP1), which constitutes the larger part of the merozoite surface protein, has been identified by researchers as a potential vaccination candidate (Holder et al., 2009). This 190–200 kDa protein is attached to the surface of the merozoite by a glycosylphosphatidylinositol at its C-terminus (Holder et al., 2009). Although it is yet unknown how the MSP1 merozoite surface complex affects erythrocyte invasion, recombinant fragments and variants of MSP1 produced from parasites have been associated with the binding of erythrocyte receptors (Kadekoppala et al., 2008). A key challenge in developing an effective malaria vaccine is the parasite’s multistage life cycle complexity. This complexity indeed imposed a significant obstacle to vaccine development, as targeting each life cycle stage requires a deep understanding of the parasite’s biology and host immune responses at various stages (Amlabu et al., 2018). The initial malaria vaccine, RTS, S (also known as Mosquirix), which is based on recombinant proteins, has demonstrated only modest effectiveness in protecting young children against the disease. (Amir et al., 2022). Despite considerable endeavors, this vaccination approach has drawbacks, as it only averts 39% of malaria infections and 30% of severe malaria cases (Amir et al., 2022). After several months, its effectiveness diminishes, requiring four doses for optimal protection (Laurens et al., 2020). This research recommends creating a peptide-based vaccine containing immune-stimulating epitopes capable of eliciting both humoral and cell-mediated immune responses to prevent malaria infection. 2. Experimental 2.1. Systematic workflow This study adhered to a systematic workflow, as depicted in Fig. 1. The diagram outlines each step involved in formulating this multi-epitope vaccine. 2.2. Protein sequence retrieval The protein sequences of Plasmodium falciparum were retrieved from the National Center for Biotechnology Information (NCBI) (Oladipo et al., 2024a) and the Universal Protein Resource (UNIPROT) Server. Apical membrane antigen 1(A0A0X8II02), Apical membrane antigen 1(A0A193PBV5), Knob associated histidine-rich protein (A0A0L7KKR3), Knob associated histidine- rich protein (W7FDY3), Merozoite surface protein 1(Q25971), Sporozoite surface protein 2(A0A0L0CV98), Sporozoite surface protein 2(A0A0L7KJ49). 2.3. Prediction of protein sequence antigenicity The antigenicity of the Plasmodium falciparum protein was predicted using ANTIGENpro (Magnan et al., 2010) and VaxiJen (Pandey et al., 2016). The threshold of ≥0.8 (ANTIGENpro) (Magnan et al., 2010), and ≥ 0.5 VaxiJen (Doytchinova et al., 2007) were considered for the selection of the protein sequence. 2.4. Cytotoxic T lymphocytes (CTL) epitopes prediction NetCTL 1.2 tool was utilized in the prediction of CTL epitopes of the proteins (Oladipo et al., 2020). The tool was set at a threshold value of 0.75, while the weights on C-terminal cleavage and TAP transport efficiency were set at 0.15 and 0.05, respectively (Zhao et al., 2017). 2.5. Helper T-Cell (HTL) epitope prediction The HTL Epitopes of the protein sequence were predicted using the Immune Epitope Database (IEDB) (Oladipo et al., 2022). Three mouse alleles, which are H2-IAb, H2-IEd and H2-IAd, were selected for the Major Histocompatibility Class II (MHC II). MHC-II affinity and percentile rank of ≤ 0.2 were used as criteria for selecting the HTL epitopes (Zhang et al., 2008). The best six epitopes were chosen for each allele. https://doi.org/10.26850/1678-4618.eq.v50.2025.e1555 https://doi.org/10.26850/1678-4618.eq.v50.2025.e1555 Original Article https://doi.org/10.26850/1678-4618.eq.v50.2025.e1555 ISSN 1678-4618 page 3/13 Figure 1. Study workflow for malaria vaccine construct design. Source: Elaborated by the authors. 2.6. B-Cell epitopes prediction B-cells are an important component of the immune system for long-term protection against pathogens and antigens (Oladipo et al., 2022). The linear B-cell Epitopes of Plasmodium falciparum protein were predicted using ABCPred server (Saha and Raghava, 2006). Eight epitopes with a score > 0.9 were selected and subjected to further analysis. 2.7. Construction of multi-epitope vaccine sequence A multi-epitope vaccine was developed by combining cytotoxic T lymphocyte (CTL), helper T lymphocyte (HTL), and B-cell epitopes, joined with an adjuvant using appropriate linkers. The adjuvant APPHALS was incorporated to enhance the vaccine's immunogenicity. AAY linkers were utilized to connect the CTL epitopes, whereas GPGPG linkers were employed to connect the HTL and B-cell epitopes (Kalita et al., 2019). 2.8. Antigenicity, allergenicity and toxicity prediction of the vaccine construct The antigenicity, allergenicity, and toxicity of the vaccine construct were assessed using the VaxiJen (Doytchinova et al., 2007), Allertop (Dimitrov et al., 2014), and Toxinpred2 (Sharma et al., 2022) servers, respectively. Antigenicity testing confirmed the vaccine's ability to stimulate antibody production. Allergenicity assessment was conducted to verify the absence of allergic reactions triggered by the vaccine. Toxicity testing was performed to ensure the vaccine's safety by confirming the absence of adverse effects on humans. 2.9. Physiochemical properties and domain identification The physicochemical properties of the vaccine construct, including molecular weight, theoretical protrusion index (PI), hydropathicity (GRAVY), aliphatic index, instability index, extinction coefficients, atomic composition, charged residues, and in vitro and in vivo half-life, were analyzed using protparam (Garg et al., 2016). 2.10. Prediction of secondary structure The self-optimized prediction method (SOPMA) was utilized to predict the secondary structure of the vaccine construct. SOPMA analyzes the amino acid composition and predicts the relationships within the construct's secondary structure (Lee et al., 2013). 2.11. Prediction of tertiary structure of the vaccine The tertiary structure of the multi-epitope vaccine construct was predicted using I-TASSER (Zhou et al., 2022). The I-TASSER tertiary structure prediction server is designed to generate protein tertiary structures and employs a quantitative scoring system to produce models. Additionally, the server predicts estimated TM- score, confidence score, standard deviation, and root mean square deviation (RMSD) values for the generated models. 2.12. Prediction of 3D configuration and discontinuous B-cell epitopes The tertiary composition of the vaccine construct was utilized to predict the 3D conformational structure of B-cell epitopes using Ellipro. Subsequently, Ellipro was employed to determine the conformational 3D structure of the predicted linear B-cell epitopes. The Ellipro results include the number of residues in each epitope, with higher residue numbers indicating greater solvent availability. The Jmol viewer was utilized for visualizing the predicted antibody epitopes (Ponomarenko et al., 2008). 2.13. Refinement of the tertiary structure To enhance the quality of the local structure in the multi- epitope vaccine construct, the Galaxy Refine web tool, based on https://doi.org/10.26850/1678-4618.eq.v50.2025.e1555 https://doi.org/10.26850/1678-4618.eq.v50.2025.e1555 Original Article https://doi.org/10.26850/1678-4618.eq.v50.2025.e1555 ISSN 1678-4618 page 4/13 the CASP 10 version, was employed (Heo et al., 2013). Galaxy Refine is a validated algorithm known for refining protein structures and improving the quality of local structural elements (Oladipo et al., 2023a). 2.14. Validation of tertiary structure The ProSA-web server was utilized to validate both the projected and refined 3D configuration of proteins, an essential step in sequence modeling. Additionally, a Ramachandran plot was obtained by submitting the PDB file of the vaccine structure to the PROCHECK server. This process serves to validate the vaccine and authenticate its potential functionality (Laskowski et al., 2006). 2.15. Molecular docking of the vaccine with toll-like receptors (TLRs) Stimulating an immune response is the primary objective in vaccine design. Therefore, it is crucial to evaluate the interactions between an antigen and Toll-like receptors (TLRs). The HDock server was employed to predict the binding sites between TLR-2 (PDB ID: 3a7c) and TLR-4 (PDB ID: 4g8a) in their most stable complex forms. Docking analysis facilitated the examination of binding affinity between the complexes, specifically the vaccine and TLRs (Yan et al., 2020). 2.16. Molecular dynamics simulation of the receptor- ligand complex The iMOD server was utilized to conduct dynamics simulations, which focused on studying the physical basis, structure, and function of biological molecules to determine the stability of the complex. Results obtained from this approach, such as deformability, eigenvalues, and covariance, provide insights into the stability of the complex (López-Blanco et al., 2014). 2.17. The in silico cloning and optimization of the vaccine protein The vaccine construct underwent codon optimization using the JCAT Java tool. This tool translates protein sequences into the expression system of another biological host to adapt the codon usage for the new host. JCAT provides the GC content and codon adaptation index (CAI) values of the adapted codons. Additionally, the tool back-translates protein sequences into DNA sequences, which are then used for silicon cloning. In this study, JCAT was employed to adjust the final vaccine sequences to fit the E. coli K12 strain expression system. The construct of the final vaccine was input into JCAT for adaptation processing (Grote et al., 2005). The DNA sequences obtained from back-translation were cloned into the E. coli K12 pET-28a (+) vector expression system at specific restriction enzyme sites, with the assistance of Snap Gene software (Li et al., 2016). 3. Result and discussion 3.1. Antigenicity prediction of Plasmodium falciparum proteins The NCBI and Uniprot servers were used to obtain the Plasmodium falciparum protein sequence for this work. A critical component of vaccinology was achieved when the chosen sequences underwent antigenic screening and were proven to be both antigenic. The antigenicity of the Plasmodium falciparum proteins retrieved was predicted, and the sequences passed the AntigenPRO server and VaxiJen server at a threshold of 0.5 and 0.8, respectively (Table 1). This shows that the sequence can elicit antibodies (Oladipo et al., 2022). The sequences that passed were then subjected to further analysis. Table 1. Selected proteins of Plasodium falciparum and their accession number. S/N PROTEIN ACCESSION NO VAXIJEN ANTIGENIC PRO 1 Apical membrane antigen 1 A0A0X8II02 0.6195 0.940153 Apical membrane antigen 1 A0A193PBV5 0.5807 0.936320 2 Knob associated histidine-rich protein A0A0L7KKR3 0.7862 0.940365 Knob associated histidine-rich protein W7FDY3 0.7880 0.922167 3 Merozoite surface protein 1 Q25971 0.5735 0.810633 4 Sporozoite surface protein 2 A0A0L0CV98 0.6279 0.902585 Sporozoite surface protein 2 A0A0L7KJ49 0.6223 0.905200 Source: Elaborated by the authors. 3.2. Prediction of novel cytotoxic T lymphocytes (CTL), helper T lymphocytes (HTL) and B-cells epitopes Different servers were employed to project CTL, HTL, and B-cell epitopes using the selected Plasmodium falciparum protein sequences that successfully passed antigenicity screening. CTL epitopes were identified based on their high scores, which fell within the threshold of 0.75 (Table 2a). Helper T lymphocytes (HTL) epitopes were also predicted, and those with low percentile ranks were chosen for further analysis (Table 2b). B-Cell epitopes falling within the threshold of 0.90 were selected (Table 2c) and incorporated into the vaccine construction alongside the HTL and CTL epitopes. The prediction of CTL, HTL, and B-Cell epitopes was conducted because a multi-epitope vaccine necessitates the inclusion of CTL, HTL, and B-Cell epitopes (Chauhan et al., 2019). This is significant because T-cells recognize surface antigens presented by MHC molecules. MHC class II molecules present surface antigens to T-helper cells, while B-cell epitopes aid in eliciting antibody and memory cell responses (Oladipo et al., 2022). The results obtained from these analyses were employed in building the vaccine candidate using appropriate linkers. https://doi.org/10.26850/1678-4618.eq.v50.2025.e1555 https://doi.org/10.26850/1678-4618.eq.v50.2025.e1555 Original Article https://doi.org/10.26850/1678-4618.eq.v50.2025.e1555 ISSN 1678-4618 page 5/13 Table 2a. Selected epitopes of CTL. Protein CTL epitopes Score CTL epitopes Score CTL epitopes Score Apical membrane antigen 1 TLDQMRHFY 32.272 AQENNGPRY 0.9554 LLSAFEFTY 17.715 ATILMVYLY 22.792 QYEQHLTDY 0.8786 DAEVAGTQY 14.733 VLATILMVY 15.535 MVSNSTCRF 0.8368 AKDKSFQNY 11.252 DISFQNYTY 15.338 VKEEYKDEY 0.8326 TLNGMRDFY 10.820 AKDISFQNY 11.520 NTETHKCEI 0.7820 MLDPEASFW 0.8858 ISDDKDSLK 11.379 IIENSNTTF 0.7664 GQNYWEHPY 0.7521 SASDQPKQY 10.603 YMGNPWTEY 17.963 Knob associated with histidine-rich protein LDEYQNQLY 11.135 YAFSEECPY 0.8247 YVPPHGAGY 10.598 Merozoite surface protein 1 HLEAKVLNY 28.435 YLKPLAGVY 12.527 QLENNVMTF 0.8661 NLEKKKLSY 19.055 LLILMLILY 10.159 KRDKFLSSY 0.8435 GIADLSTDY 15.242 QTEDNYASL 0.8765 ESIQTEDNY 0.7781 Sporozoite surface protein 2 LLACAGLAY 24.876 LNENAIHLY 14.074 LTDGIPDSI 0.9307 Table 2b. Selected epitopes of HTL. Protein Alleles HTL epitope Score Alleles HTL epitope Score Apical membrane antigen 1 H2-IAb KDGGFAFPPTKPLMS 0.26 H2-IAd IIIASSAAVAVLATI 0.73 H2-IAb DGGFAFPPTKPLMSP 0.37 H2-IAb DLKDGGFAFPPTKPL 0.84 H2-IAb GGFAFPPTKPLMSPM 0.39 H2-IAd DNMKIIIASSAAVAV 0.85 H2-IAb SMIKSAFLPTGAFKA 0.39 H2-IAb GFAFPPTKPLMSPMT 0.87 H2-IAb MIKSAFLPTGAFKAD 0.40 H2-IAd IIASSAAVAVLATIL 0.90 H2-IAd MKIIIASSAAVAVLA 0.46 H2-IAd PTYDNMKIIIASSAA 0.91 H2-IAb IKSAFLPTGAFKADR 0.48 H2-IAd YDNMKIIIASSAAVA 0.93 H2-IAd KIIIASSAAVAVLAT 0.58 H2-IAd KPTYDKMKIIIASSA 0.37 H2-IAb LKDGGFAFPPTKPLM 0.58 H2-IAb KSAFLPTGAFKADRY 0.60 H2-IAb KSAFLPTGAFKADRY 0.60 Knob associated histidine-rich protein H2-IEd ENGPNIFALRKRFPL 0.65 H2-IEd QENGPNIFALRKRFP 1.75 H2-IEd GPNIFALRKRFPLGM 0.79 H2-IAb GSTTGATTGANAVQS 1.65 H2-IEd PNIFALRKRFPLGMN 0.84 H2-IAb STTGATTGANAVQSK 1.70 H2-IEd SFKNKNTLRRKKAFP 1.65 H2-IAb AGSTTGATTGANAVQ 1.95 H2-IEd FKNKNTLRRKKAFPV 1.75 H2-IAb TTGATTGANAVQSKD 1.95 Merozoite surface protein 1 H2-IAb KNKNYTGNSPSVNNT 1.00 H2-IAb KNYTGNSPSVNNTDV 1.51 H2-IAb IKNKNYTGNSPSVNN 1.10 H2-IEd KDPYKFLNKEKRDKF 1.90 H2-IAb VIKNKNYTGNSPSVN 1.10 H2-IEd DPYKFLNKEKRDKFL 2.00 H2-IAb NKNYTGNSPSVNNTD 1.26 Sporozoite surface protein 2 H2-IAb LAYKFVVPGAATPYA 0.10 H2-IAb DRYIPYSPLPPKVLD 0.23 H2-IAb AGLAYKFVVPGAATP 0.12 H2-IAb RYIPYSPLPPKVLDN 0.30 H2-IAd KNKEKALIIIKSLLS 1.90 H2-IAb YKFVVPGAATPFAGE 0.46 H2-IAd NKEKALIIIKSLLST 2.00 H2-IAb KFVVPGAATPFAGEP 1.95 H2-IAb LAYKFVVPGAATPFA 0.10 Table 2c. Selected epitopes of B-cell. Protein B-cell epitope Position Score Apical membrane antigen 1 RFFVCKCVERRAEVTS 355 0.92 MKIIIASSAAVAVLAT 397 0.90 AFKADRYKSHGKGYNW 236 0.90 MDEPQHYGKSNSRNDE 579 0.93 ADIPEHKPTYDKMKII 533 0.91 Knob-associated histidine-rich protein DNKGSEGYGYEAPYNP 290 0.90 Merozoite surface protein 1 SESGSDTLEQSQPKKP 100 0.90 Sporozoite surface protein 2 DRYIPYSPLPPKVLDN 410 0.94 ERKQSDPQSQDNNGNR 426 0.92 PEDSEKEVPSDVPKNP 365 0.91 HGRNNENRSYNRKYND 454 0.90 AGLAYKFVVPGAATPF 511 0.90 Source: Elaborated by the authors. https://doi.org/10.26850/1678-4618.eq.v50.2025.e1555 https://doi.org/10.26850/1678-4618.eq.v50.2025.e1555 Original Article https://doi.org/10.26850/1678-4618.eq.v50.2025.e1555 ISSN 1678-4618 page 6/13 3.3. Construction of novel multiple epitope subunit vaccine A multi-epitope vaccine was assembled using the predicted CTL, HTL, and B-cell binding epitopes. Linkers were utilized to connect these epitopes (Oladipo et al., 2022), and an adjuvant was linked to the construction to enhance its potency and elicit a robust immune response (Oladipo et al., 2022). Specifically, AAY linkers were employed to connect CTL epitopes, GPGPG linkers were used to link HTL and B-cell epitopes, and EAAAK linkers were utilized to attach the adjuvant to the vaccine construct (Ito et al., 2017) as shown in Fig. 2. Figure 2. The schematic representation of the malaria vaccine construct. Source: Elaborated by the authors. 3.4. Physicochemical properties of the vaccine construct The physicochemical properties of the vaccine construct predicted by Protparam indicate that the vaccine has a molecular weight of 15.21 kDa, falling within the range for an accepted vaccine candidate (Garg et al., 2016). Our findings suggest that the vaccine construct is antigenic, non-toxic, and non-allergenic, indicating its safety. The theoretical pI value of 8.94 suggests a structurally favorable vaccine. The aliphatic index score of 60.01 indicates the presence of aliphatic side chains in the vaccine. In contrast, the instability index score of 31.66 predicts the vaccine to be stable (Oladipo et al., 2023b). The GRAVY result of -0.385 indicates the hydrophobicity of immunization (Oladipo et al., 2024b). Furthermore, the predicted half-life is 4.4 hours in mammalian reticulocytes in vitro, >20 hours in yeast, and 10 hours in E. coli in vivo. 3.5. Projection of secondary structure SOPMA was employed to predict the secondary structure of the constructed vaccine. The server provided additional information on the vaccine construct, revealing an alpha helix content of 25.87%, an extended strand content of 15.55%, a random coil content of 54.04%, and a beta turn structure content of 4.55% (Fig. 3 and Table 3). The high percentage of the random coil suggests a concentrated presence of epitopes at that point (Tahmoorespur et al., 2017). The prediction of the secondary structure of our vaccine indicates stability, good flexibility, and a globular conformation, which aligns with the findings of Oluwagbemi et al. (2022). Table 3. Parameter of the secondary structure prediction. Parameters No of Residues Percentage Alpha helix 381 25.87% 310 helix 0 0 pi helix 0 0 Beta bridge 0 0 Extended strand 229 15.55% Beta turn 0 0 Bend region 0 0 Random coil 796 54.04% Ambiguous states 0 0 Other state 0 0 Source: Elaborated by the authors. Figure 3. Prediction of the secondary structure of the constructed vaccine. Source: Elaborated by the authors. https://doi.org/10.26850/1678-4618.eq.v50.2025.e1555 https://doi.org/10.26850/1678-4618.eq.v50.2025.e1555 Original Article https://doi.org/10.26850/1678-4618.eq.v50.2025.e1555 ISSN 1678-4618 page 7/13 3.6. Prediction of the tertiary structure of the constructed vaccine The I-TASSER server was employed to predict the 3D configuration of the multi-epitope vaccine construct. Five models were expected, but model 1 was chosen based on the confidence score of 0.19, the estimated TM-score of 0.74 ± 0.11, and the Root Mean Square Deviation (RMSD) of 9.4 ± 4.6 Å (Fig. 4). The B-cell conformational epitope for the vaccine construct was identified using the Ellipro server (Fig. 5). Figure 5. The 3D model of the 5 predicted conformational B-cell epitopes of the final vaccine construct. The yellow region are the conformational B-cell epitopes, while the grey areas are the residue remnant (a) 205 residue has a score of 0.852 (b) 165 residue has a score of 0.811 (c) 125 residue has a score of 0.742 (d) 24 residue has a score of 0.667 (e) 25 residue has a score of 0.645. Source: Elaborated by the authors. 3.7. Refinement of the tertiary structure Refinement of the 3D configuration entails reconstructing protein side chains, molecular dynamics simulation, and repacking to enhance the vaccine's tertiary structure. The Galaxy Refine web server was utilized for refining the vaccine's configuration, resulting in the prediction of five refined models. Model 1 was selected based on specific criteria, including a GDT-HA score of 0.8880, RMSD of 0.597, MolProbity score of 2.712, Clash score of 27.0, Poor rotamers of 0.7, and Rama favored of 75.1. 3.8. Validation of tertiary structure Refinement provides a refined 3D vaccine model having a higher number of residues in the favored region (67.1.0%), 21.5% in the allowed area and 6.1% found in the disallowed region of Ramachandran plot (Fig. 6 and 7). Validation is often carried out to recognize errors within the structure of the final vaccine model. ProSA and PROCHECK servers provided a Z-score value of −4.07 (Fig. 8), which indicates the stability of the model. Structural validation scores obtained from ERRAT and ProSA tools proved that the overall quality of the vaccine construct meets the requirement (Oladipo et al., 2022). Figure 6. Ramachandran plot showing favored region of the vaccine. Source: Elaborated by the authors. Figure 4. Tertiary structure of vaccine construct. Figure 7. Showing the favored region and number of residues. Source: Elaborated by the authors. https://doi.org/10.26850/1678-4618.eq.v50.2025.e1555 https://doi.org/10.26850/1678-4618.eq.v50.2025.e1555 Original Article https://doi.org/10.26850/1678-4618.eq.v50.2025.e1555 ISSN 1678-4618 page 8/13 Figure 8. Graph showing model quality of the vaccine. Source: Elaborated by the authors. 3.9. Molecular docking of the tertiary structure The Tertiary structure was subjected to molecular docking using HDock server. The binding energy and molecular relationship of the multi-epitope subunit vaccine with TLR-2 (3a7c) and TLR-4 (4g8a) were probed by molecular docking (Fig. 9). One model was selected from each of the docked complexes based on their proper receptor interactions, low binding energy and center energy scores (Pandey et al., 2016). The TLR 2 and TLR 4 have binding energies of -305.14 and -303.77, respectively (Table 4), which shows that the receptors have a high binding energy with the vaccine construct. This low binding energy score indicates strong affinities between the molecules (Oladipo et al., 2023c). Figure 9. Molecular Docking of vaccine with TLRs (a) TLR-2 and vaccine construct (b) TLR-4 and vaccine construct. Source: Elaborated by the authors. Table 4. Molecular Docking against TLR-2 and TLR-4. Dock result (rank) TLR-4 TLR-2 Docking score -303.77 -305.14 Confidence score 0.9559 0.9578 Source: Elaborated by the authors. 3.10. Molecular dynamic simulation Standard Mode Analysis (NMA) was performed on the selected docked vaccine-receptor complex to investigate stability and mobility using the iMODs server. The vaccine protein and its receptor were predicted to rotate towards each other, as depicted in Fig. 10 and 11 for TLR2 and TLR4, respectively. Hinges in regions of high deformability indicate the deformability of the vaccine-receptor complex, as shown in Fig. 10c and 11c for TLR2 and TLR4, respectively. The B-factor is directly proportional to the RMS value inferred through NMA (Fig. 10d and 11d). Eigenvalues for the vaccine-receptor complexes obtained from the iMODs server were 6.01e-09 and 4.75e-09 for TLR2 and TLR4, respectively (Fig. 10e and 11e). Variance is inversely proportional to the Eigenvalue. Residual index graphs indicate correlated, anti- correlated, and uncorrelated pairs of residues in the variance matrix, represented by red, blue, and white colors, respectively (Fig. 10b and 11b). The elastic network model generated by iMODs (Fig. 10f and 11f) illustrates pairs of atoms connected by springs, with stiffer springs represented by dark grey areas. Dynamics results showed positive eigenvalues (2.27e05; 2.06e-06; 2.03e-05; 1.53e-05), which are significant for vaccine stability and rotation, consistent with previous studies (Chauhan et al., 2019). https://doi.org/10.26850/1678-4618.eq.v50.2025.e1555 https://doi.org/10.26850/1678-4618.eq.v50.2025.e1555 Original Article https://doi.org/10.26850/1678-4618.eq.v50.2025.e1555 ISSN 1678-4618 page 9/13 (a) (b) (c) (d) (e) (f) Figure 10. Molecular dynamics simulation for TLR2: (a) Spin prediction of the ligand-receptor interaction; (b) Covariance map of the ligand-receptor interaction (c) Deformability B-factor region of the ligand-protein interaction (d) Mobility B-factor of the ligand-protein interaction (e) Eigenvalues of the ligand-receptor interaction (f) Elastic network of the ligand-protein interaction. Source: Elaborated by the authors. https://doi.org/10.26850/1678-4618.eq.v50.2025.e1555 https://doi.org/10.26850/1678-4618.eq.v50.2025.e1555 Original Article https://doi.org/10.26850/1678-4618.eq.v50.2025.e1555 ISSN 1678-4618 page 10/13 (a) (b) (c) (d) (e) (f) Figure 11. Molecular dynamics simulation for TLR2: (a) Spin prediction of the ligand-receptor interaction (b) Covariance map of the ligand-receptor interaction (c) Deformability B-factor region of the ligand-protein interaction (d) Mobility B-factor of the ligand-protein interaction (e) Eigenvalues of the ligand-receptor interaction (f) Elastic network of the ligand-protein interaction. Source: Elaborated by the authors. 3.11. Codon adaptation and in silico cloning Integrating the malaria vaccine construct into the E. coli expression system necessitates using JCAT and SnapGene servers. Adapting the vaccine to the E. coli K12 strain predicted a GC content of 59.31%, a Codon Adaptation Index (CAI) of 0.91. It translated the protein sequence back to nucleotides compatible with E. coli codons. The back-translated nucleotide sequence was incorporated into the E. coli expression system using the restriction enzymes ECORI (192) and BAMHI (4106) as cloning sites (Fig. 12). The solubility of the overexpressed recombinant protein in the E. coli host is crucial for biochemical and functional investigations. Therefore, adapting the vaccine model into an E. coli expression system is an essential step in vaccine design, and codon adaptation is a preferred method for achieving efficient expression of foreign genes in a host. This is because when the codons used by the host differ from those of the organism's genes, lower expression rates may occur if the genes are not adapted. Hence, we adapted the final vaccine protein sequences to the E. coli strain K12 using the JCAT server and obtained satisfactory results. JCAT also back translated the protein sequences to nucleotides, which were then cloned into the E. coli pET28a (+) vector using the ECORI (192) and BAMHI (4106) restriction sites, resulting in a total clone length of 9.2 Kbp. The target sequence was encoded between 6-histidine residues, which would facilitate purification purposes. https://doi.org/10.26850/1678-4618.eq.v50.2025.e1555 https://doi.org/10.26850/1678-4618.eq.v50.2025.e1555 Original Article https://doi.org/10.26850/1678-4618.eq.v50.2025.e1555 ISSN 1678-4618 page 11/13 Figure 12. In silico cloning for adapted vaccine into pET28a (+) vector. Source: Elaborated by the authors. 4. Conclusions Tropical regions characterized by high temperatures and humidity, particularly in South Asia and Africa, are highly susceptible to malaria. Achieving malaria eradication requires the implementation of innovative control techniques. This study utilized protein sequences eliciting various T-cell (HTL and CTL) and B-cell epitopes in an immunoinformatics approach to develop a potential vaccine. The results of this investigation demonstrate that the final construct has successfully fulfilled the designed criteria for malaria vaccine development. However, this computational work necessitates experimental validation. In vitro and in vivo tests are essential to assess the immunogenicity and safety of the potential vaccine. Authors’ contribution Conceptualization: Elijah Kolawole Oladipo; Data curation: James Akinwumi Ogunniran; Formal Analysis: Samuel Nzube Nwosu; Funding acquisition: Not applicable; Investigation: James Akinwumi Ogunniran; Methodology: James Akinwumi Ogunniran; Project administration: Not applicable; Resources: Not applicable; Software: Kehinde Oluyemi Ajayi; Oluseyi Rotimi Taiwo; Supervision: Elijah Kolawole Oladipo; Validation: Olaoluwa Kehinde Alao; Adeola Christianah Ogunwole; Visualization: Kemiki Olalekan Ademola; Michael Asebake Ockiya; Writing – original draft: James Akinwumi Ogunniran; Caleb Enejoh Omede; Writing – review & editing: James Akinwumi Ogunniran; Anthony Godswill Imolele. Data availability statement All datasets analyzed in this research are all publicly available at the National Centre of Biotechnology Information (NCBI) and the Universal Protein Resource (UNIPROT) Server. It could be made available upon request. Funding Not applicable. Acknowledgments We appreciate Helix Biogen Institute, Ogbomoso, Oyo State, Nigeria for their technical support during this research work. Conflict of interest The authors declare that there is no conflict of interest. References Amir, A.; Parisa, V.; Somayeh, J.; Gholamreza, A. S. A multi-epitope vaccine designed against blood-stage of malaria: an immunoinformatic and structural approach. Sci. Rep. 2022, 12, 11683. https://doi.org/10.1038/s41598-022-15956-3 Amlabu, E.; Mensah-Brown, H.; Nyarko, P. B.; Akuh, O. A.; Opoku, G.; Ilani, P.; Oyagbenro, R.; Asiedu, K.; Aniweh, Y.; Awandare, G. A. https://doi.org/10.26850/1678-4618.eq.v50.2025.e1555 https://doi.org/10.26850/1678-4618.eq.v50.2025.e1555 https://doi.org/10.1038/s41598-022-15956-3 Original Article https://doi.org/10.26850/1678-4618.eq.v50.2025.e1555 ISSN 1678-4618 page 12/13 Functional Characterization of Plasmodium falciparum Surface-Related Antigen as a Potential Blood-Stage Vaccine Target. 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