Introduction

The recent outbreak of Ebola virus in Sub-Saharan Africa has once again proven that viral diseases pose a major threat to human society. There is no effective cure for most of the viral diseases. Most of them are treated symptomatically and death toll is always high. The only real action plan against viral diseases is the development of effective vaccines to prevent them. Vaccines are being constantly developed for viral diseases and many effective vaccines exist against yellow fever, measles, rubella, mumps, hepatitis B, influenza, human papillomavirus, polio, rabies etc. Still many old and newly emerging viral diseases remain a threat without proper vaccination. Examples include HIV (Human Immunodeficiency Virus) and Ebola virus.

Ebola virus belongs to the Group V (−)ssRNA, Order Mononegavirales, Family Filoviridae, Genus Ebolavirus, and Species Zaire ebolavirus. It was first identified in Democratic Republic of Congo (formerly Zaire) and it is named as such. It was first suspected to be a new strain of the closely related Marburg virus but was renamed to Ebola virus in 2010 (Feldmann et al. 2003; Peters et al. 1995; Ascenzi et al. 2008). The recent outbreak of the virus in West Africa has been responsible for more than 10,000 casualties so far (WHO 2015). Fruit bats are considered as the natural host of the virus and it is transmitted mainly through bodily fluids to human beings and other primates (Leroy et al. 2005; Pourrut et al. 2005; Funk and Kumar 2015; Drazen et al. 2014). Ebola virus disease (EVD), also known as Ebola hemorrhagic fever is a severe illness in humans. It is fatal without proper treatment. Recovery chances are really low, with the reported mortality rates being as high as 90 % (Sanchez et al. 2006). The current outbreak in West Africa has mortality rate of 70 % (WHO Ebola Response Team 2014). Ebola spreads through humans via direct contact with the blood, secretions, organs or other bodily fluids of infected people, and with objects contaminated with these fluids like bedding or clothing. According to a report published by the World Health Organisation (WHO) in September, 2014, health-care workers are frequently infected while treating patients with EVD. This occurs through close contact with patients without adequate precautions. Burial ceremonies can also play a role in the transmission of Ebola. Men who have recovered from the disease can still transmit the virus through their semen for up to 7 weeks after recovery from illness. Women can transmit the virus to children through breast milk. Symptoms of the disease occur in a specific order. First discernible symptoms are fever fatigue, muscle pain, sore throat and headache. This is followed by vomiting, diarrhea, rash, symptoms of impaired kidney and liver function, both internal and external bleeding. Laboratory findings include low white blood cell and platelet counts and elevated liver enzymes.

For prevention and cure, few vaccines have been developed and tested on non-human primates. These vaccines are either attenuated recombinant vesicular stomatitis virus vectors expressing the EBOV glycoprotein or an adenoviral vector encoding the Ebola glycoprotein (GP) (Sullivan et al. 2006; Geisbert et al. 2008; Jones et al. 2005). Both of these vaccines have been found promising in initial testing on non-human primates. These results demonstrate that it is indeed possible to develop a vaccine against Ebola virus (Sullivan et al. 2000, 2003). Currently, experimental drug treatments are being made available to impede Ebola outbreak. The major ones include; Zmapp, a mixture of three monoclonal antibodies that attack proteins on the surface of the virus (Qiu et al. 2014). Another drug TKM-Ebola has been designed to target strands of genetic material of the virus (Geisbert et al. 2010). The drug interrupts the genetic code of the virus and prevents it from making disease-causing proteins (Keller and Stiehm 2000). The US-based pharmaceutical company, Sarepta therapeutics, has developed a similar RNA treatment (Iversen et al. 2012). These drugs have tested on a small number of healthy volunteers but rarely on human patients. So far, no drug or vaccine has been approved by the FDA for the treatment.

Using existing knowledge about the structure and function of Ebola genome, Glycoprotein 2 (GP2) and Viral protein 24 (VP24) have been chosen as targets for vaccine development against this deadly virus (Lee et al. 2008; Huang et al. 2002). GP2 subunit of the virus has been found to be responsible for fusion of viral and host cell membrane (Volchkov et al. 1998). Cyrstallography studies have revealed that GP2 contains a central triple-stranded coiled coil followed by a disulfide-bonded loop which is homologous to an immunosuppressive sequence in retroviral glycoproteins (Malashkevich et al. 1999; Weissenhorn et al. 1998). The fusion peptides near the N termini form disulfide-bonded loops at one end of the molecule and that the C-terminal membrane anchors are at the same end, which possibly may help in initiation of fusion of membranes (Weissenhorn et al. 1998; Takada et al. 1997). The fusion active conformation of the subunit resembles to that of other viruses such as HIV and Influenza (Weissenhorn et al. 1998; Lee and Saphire 2009).

VP24 is a secondary matrix protein and is a minor component of virions. It possesses structural features commonly associated with viral matrix proteins (Han et al. 2003). It is chiefly responsible for the virus being able to evade the antiviral immune response of the body by suppressing the interferon (IFN) production. VP24 has been shown to compete with STAT1 to bind karyopherin α1, blocking nuclear accumulation and leading to inhibition of IFN signaling (Reid et al. 2006; Amarasinghe et al. 2014). VP24 is also responsible for correct assembly of a functional nucleocapsid and plays a role in virus assembly and budding (Han et al. 2003).

Biochemical, serological, and microbiological methods have been used to dissect pathogens and identify the components useful for vaccine development. Since the most abundant proteins are most often not suitable vaccine candidates, and the genetic tools required to identify the less abundant components maybe inadequate or not available at all, this approach can take years or even decades (Sette and Rappuoli 2010). In 1995, J. Craig Venter published the genome of the first free living organism, Haemophilus influenzae (a pathogenic bacterium) (Fleischmann et al. 1995). This opened a new way of using computers to rationally design vaccines by using the information present in the genome without going through the traditional microbiological and biochemical route. This new approach was called “Reverse Vaccinology” (Sette and Rappuoli 2010). The first example of reverse vaccinology approach was the development of a vaccine against serogroup B Neisseria meningitidis (MenB),a pathogen that causes 50 % of the meningococcal meningitis worldwide. In this study, bioinformatics methods were first used to screen the complete genome of MenB strain MC58, for genes encoding putative surface exposed or secreted proteins. In total, 350 novel vaccine candidates were predicted and expressed in E. coli; 28 were found to elicit protective immunity. It took less than 18 months to identify more and some novel vaccine candidates in MenB than had been discovered during the past 40 years by conventional methods (Pizza et al. 2000).

The approach of computers in this way for vaccine design is termed as “Immunoinformatics”. It mostly focuses on the design and study of algorithms for mapping potential B cell and T-cell epitopes, hence speeding up the time and lowering the cost needed for laboratory analysis of pathogen gene products (Doytchinova et al. 2003; Patronov and Doytchinova 2013). This concept also provides us with the concept of the “Immunome”, which can be defined as the set of antigens or epitopes that interface with the host immune system (Sette et al. 2005; De Groot and Berzofsky 2005). Thus immunomics bridges the discipline of genomics and proteomics by involving the immune system and focuses on elucidating the set of antigens that interact with the host immune system and the mechanisms involved in these interactions (Rinaudo et al. 2009).

Materials and Methods

Retrieval of Protein Sequences

The required protein sequences of GP2 and VP24 proteins from various strains of the Ebola virus. Table 1 lists all the sequences along with strains and Uniprot/GenBank accession numbers. The protein sequences belong to all the strains that have been found in various Ebola virus incidents throughout the world since its discovery. The information regarding different strains and their proteomes was acquired from Viral Bioinformatics Resource Center (www.biovrus.org). The sequences were stored as two fasta files containing multiple sequences for each protein respectively.

Table 1 Protein sequences of GP2 and VP24 retrieved from UniprotKB and NCBI GenBank
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Control

The matrix protein 1 [Influenza A virus (H5N1)] was taken as control as it a well-studied viral antigen showing proper immune response in humans. It has been tested as an adjuvanted virosomal H5N1 vaccine and found to induce a balanced Th1/Th2 CD4(+) T cell response in man (Pederson et al. 2014). It was subjected to all the in silico procedures in this study to prove that the pipeline is adequate for antigenic predictions.

Multiple Sequence Alignment (MSA)

Multiple sequence alignment was used to identify conserved regions in the protein sequences of GP2 and VP24 respectively. Amino acid sequences which are found to be conserved among different strains signify less ability of the protein to mutate in that region. They also provide good starting point for the analysis of antigenic sequences and lymphocytic epitopes since any vaccine designed using such conserved sequences should work on all the strains. PRALINEWWW was used to perform multiple alignment of the proteins (Simossis and Heringa 2005). The tool is available in form of a web application at “www.ibi.vu.nl/programs/pralinewww/”. Praline is a highly customizable MSA application (Simossis and Heringa 2005; Heringa 2002). It provides many different alignment strategies such as progressive alignment, integration of structural features such as secondary structures and trans-membrane regions (Simossis and Heringa 2005). The output can be obtained in tree form or in the form of a fasta file containing the alignment. For our purpose, we used BLOSUM62 as the weight matrix for the alignment. Gap opening and extension penalties were chosen to be 12 and 1 respectively. The alignment strategy incorporated was PSI-BLAST pre-profile processing (Homology-extended alignment) with 3 PSI-BLAST iterations at an E-value cut-off of 0.01 (Simossis et al. 2005). The alignment was made against the NCBI NR (non-redundant) database. DSSP-defined secondary structure searching along with secondary structure prediction using PSIPRED was also used (Heringa 1999). And the output was generated as a fasta file containing the multiple alignment.

Antigenicity Prediction

Antigenicity prediction of all the conserved sequences generated in the previous step was performed to assess their overall possible role in generating immune response. Vaxijen server (http://www.ddg-pharmfac.net/vaxijen/VaxiJen/VaxiJen.html) was used as it does not rely on sequence similarities with known antigens (Doytchinova and Flower 2007). This provides with a unique insight into potentially novel antigenic sequences which may not have obvious sequence similarities. This also makes it a very useful tool for small sequences (as in this case), since sequence similarity predictions depend on the overall length of the sequences. And small sequences may generate many localized hits which may be irrelevant. The vaxijen server gives results in form of probability scores, prediction threshold for which was kept at 0.5 for getting the accuracy of 87 % (Gededzha et al. 2014).

T-Cell Epitope Prediction for MHC I and MHC II

The involvement of short sequences of amino acids in many processes of molecular biology such as the binding of immunogenic peptides to major histocompatibility complex (MHC) molecules is well established. Reliable predictions of immunogenic peptides can minimize the experimental effort needed to identify new epitopes to be used in vaccine design. NetCTL (http://www.cbs.dtu.dk/services/NetCTL/) is a web-based tool for predicting human cytotoxic T lymphocyte (CTL) epitopes in any given protein. It does so by integrating predictions of proteasomal cleavage, TAP transport efficiency, and MHC class I affinity. It is a highly sensitive tool which performs better than other tools in large scale comparisons (Larsen et al. 2007). It includes 12 MHC I supertypes in its prediction protocol. 0.15 was used as the threshold for C terminal cleavage, 0.05 for TAP transport efficiency and 0.5 for epitope prediction as these values increase sensitivity to a larger extent than they lower the specificity of the prediction (Nielsen et al. 2003, 2005; Peters et al. 2003). The peptides which were selected in the anitgenicity prediction were used as the input.

Antigen presenting cells (APCs) present peptides from the extra cellular space to T helper cells, which are activated if the peptides are recognized as non-self. The peptides are presented on the cell surface in complex with major histocompatibility class II (MHC II) molecules. Thus, prediction of peptides that bind to MHC II is a very important step for in silico vaccine design. The MHC class II binding groove, being open at both ends, makes the correct alignment of a peptide in the binding groove an important part of identifying the core of an MHC class II binding peptide. MHC II binding prediction was performed by using the IEDB MHC II prediction tool at http://tools.immuneepitope.org/mhcii/. It uses a novel Stabilization matrix alignment method (SMM-align) (Nielsen et al. 2007).

B-Cell Epitope Prediction

One of the key aspects of the immune system is the antibody-mediated identification of foreign, infectious objects, such as bacteria and viruses. Antibodies bind to antigens at sites known as B-cell epitopes. Ability to identify these binding areas in the antigen sequence or on its surface is important for the development of vaccines. The linear B-cell epitope is a short segment in the amino acid sequence of the antigen. There are structural B cell epitiopes which are non-contiguous and depend on the 3D structure of the antigens. The reason for linear epitope prediction is that it is computationally more feasible. Since this work is based on sequence information and does not focus on the structures of GP2 and VP24, we focus on the conserved linear peptides of the proteins which are established through MSA and not on the structural information. LBTope (http://www.imtech.res.in/raghava/lbtope/index.php) was used to perform this prediction. It uses very large datasets of 14,876 B-cell epitope and 23,321 non-epitopes of variable length, 12,063 B-cell epitopes and 20,589 non-epitopes of fixed length and 1042 epitopes and 1795 non-epitopes where each epitope or non-epitope has been experimentally validated (Singh et al. 2013). Availability of large datasets increases the validity of machine-learning predictive algorithms such as ANN. To increase the specificity of prediction, the probability was increased to 80 %.

Population Coverage Analysis of MHC I Epitopes

The MHC is a highly polymorphic group of genes. Different populations typically express different repertoires of MHC alleles. An epitope-based vaccine can include only a limited number of peptides due to economic and regulatory issues. Hence, it is very important to identify the optimal set of peptides for a vaccine. Constraints such as peptide mutation rates and maximum number of selected peptides place an additional burden on the overall design process (Toussaint and Kohlbacher 2009). Optitope (http://etk.informatik.uni-tuebingen.de/optitope) predicts the overall immunogenicity of a peptide set which depends on the individual immunogenicities of each peptide with respect to the MHC alleles in a given population. North Africa, South East Asia, South West Asia, Sub Saharan Africa were reported by the WHO to have Ebola virus breakout sometimes in the past. These populations were used for the analysis. The analysis focused on MHC I because of the fact that viral peptides are presented only on MHC I via the endogenous pathway.

Docking of the Selected Epitopes with MHC Alleles

Molecular docking is a key tool in structural molecular biology and computer-assisted drug design. The goal of ligand–protein docking is to predict the predominant binding mode of a ligand with a protein of known three-dimensional structure. It is a hypothesis generating procedure which provides a basis for further in vitro/in vivo analysis. It was used to analyze the binding of the selected MHC I and MHC II epitopes with the 3D structures of respective MHC molecules. The epitopes were selected for both MHC I and MHC II on the basis of their binding scores in the predictions. These epitopes were modeled by using the short peptide folding method of PEP-FOLD at the mobyle web server (Thévenet et al. 2012; Maupetit et al. 2009, 2010). The 3D structures of the MHC I HLA-A2 (pdb id: 3MRE) and MHC2 HLA-DR1(pdb id: 1AQD) were downloaded from the PDB server and modified for further usage as receptors.

Autodock vina, developed by the Scripps Research Institute, was used for docking the epitopes with the MHC molecules (Trott and Olson 2010). It was developed as an improvement over the original autodock program both in term of accuracy and speed. Autodock vina can use multicore processors and hence is much faster than the original autodock.

Results

Selection of Conserved Sequences

Multiple sequence alignment by PRALINEWWW led to the discovery of conserved sequences in the GP2 and VP24 proteins. 13 conserved regions were found in GP2. Out of these, two were discarded for being too short (nine and seven residues respectively). The selection criterion was the conservation rating of seven or above in the PRALINEWWW conservation colour chart. Secondary structure info generated by PRALINEWWW (PSIPRED) was checked to ensure that there were no gaps in the defined helices or strands. Secondary structure info was annotated in the fasta headers of the conserved sequences.

11 conserved regions were found in VP24. Out of these, one was discarded for being too short (seven residues). The selection criterion was kept the same. In this case most of the sequences were edited to avoid introducing gaps in conserved helices and strands according to the secondary structure info generated. Table 2 lists the conserved sequences found in both the proteins.

Table 2 Conserved sequences from GP2 and VP24 of Ebola virus along with antigenicity scores by VaxiJen
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Antigenicity of the Conserved Sequences

Analysis revealed that eight and five conserved sequences, respectively, from GP2 and VP24 proteins met the criteria of default threshold level, ≥0.5, in VaxiJen as listed in Table 2. The control antigen also tested positive in the vaxijen server.

T-Cell Epitope Prediction for MHC I and MHC II

NetCTL prediction tool covering all supertypes created a total of 160 and 34 nonamers from the conserved sequences of GP2 and VP24 proteins, respectively based on the tool’s combined score threshold. Further analysis revealed 76 unique epitopes reacting with 12 MHC I alleles in GP2 and 19 unique epitopes reacting with 12 MHC I alleles in VP24 as listed in Table 3. In the control antigen, 188 MHC I and 123 MHC II epitopes were predicted using the same tools.

Table 3 Most probable predicted epitopes interacting with different MHC class I alleles
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IEDB MHC II epitope prediction tool generated 72 unique binding peptides from the GP2 protein having affinity values <250 nM which reacted to 36 unique HLA DP, DQ, and DR alleles and 40 unique binding peptides from the VP24 protein having affinity values <250 nM which reacted to 30 unique HLA DP, DQ, and DR alleles as listed in Table 4.

Table 4 Most probable predicted epitopes interacting with different MHC class I alleles
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B-Cell Epitope Prediction

According to the criteria set for the prediction of B cell epitopes using LBTope server along with the basis of VaxiJen scores, GP2 protein was predicted to have four conserved peptides and VP24 protein was predicted to have two conserved peptides to contain B-cell epitopes. Similar criteria set was used for the control antigen and 31 B cell epitopes were predicted by LBTope.

Population Coverage Analysis of MHC I Epitopes

Over a thousand different human MHC (HLA) alleles are known and different HLA types are expressed at different frequencies in different ethnicities. Identified epitopes that bind to several MHC alleles would be considered as the best probable epitope only if their combined frequency in a population shows good coverage by approaching 100 % or close to 100 %.

Opitope prediction server found FLYDRLAST, LFLRATTEL from the GP2 protein and NYNGLLSSI from the VP24 protein. FLYDRLAST showed interaction with HLA-A*0201 in North African, South West Asian and Sub Saharan African populations and LFLRATTEL showed interaction with HLA-A*2402 in South East Asian Population. NYNGLLSSI showed interaction with both HLA-A*0201 and HLA-A*2402 in all the four target populations.

Docking of the Selected Epitopes with MHC Alleles

Using AutoDock Vina, binding models of predicted epitopes to their respective HLA molecules (both class I and class II) were generated (Figs. 1, 2). In case of class 1, epitopes FLYDRLAST and LFLRATTEL from GP2 protein bound to the binding groove of HLA-A2 (pdb id 3MRE) with the binding energies of −7.8 and −8.5 kcal/mol respectively (Fig. 1a, b). Furthermore, FLYDRLAST forms a single hydrogen bond with the residue ASP77 having a bond length of 2.07 Angstrom and LFLRATTEL forms three hydrogen bonds with residues ASP77, LYS146 and ARG97 having bond lengths of 1.812, 2.143 and 2.145 Angstrom respectively. The epitope NYNGLLSSI from the VP24 protein bound to the binding groove with the binding energy of −7.7 kcal/mol (Fig. 1c) and forms two hydrogen bonds with residues ASP77 and HIS114 having bond lengths of 2.108 and 2.249 Angstrom respectively. The selected MHC I epitope from the control antigen, GMLGFVFTL bound to the binding groove of the same HLA molecule as above. It showed a binding energy of −7.4 kcal/mol and also formed a single hydrogen bind with THR73 with a bond length of 2.182 Angstrom (Fig. 3a).

Fig. 1
figure 1

Docking interactions of MHC I epitopes. a and b are from GP2 while c is from VP24. Interaction details are given in results

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Fig. 2
figure 2

Docking interactions of MHC II epitopes. a is from GP2 and b is from VP24. Interaction details are given in results

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Fig. 3
figure 3

Docking interactions of the control antigen MHC peptides. a is with MHC I and b is with MHC II. Interaction details are given in results

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In case of class 2, epitope EGAFFLYDRLASTVI from GP2 protein bound to the binding groove of HLA-DR1 (pdb id 1AQD) with the binding energy of −6.2 kcal/mol (Fig. 2a). It forms four hydrogen bonds with residues ASN62, SER53, GLN9 and THR77 having bond lengths of 2.125, 2.165, 1.93 and 2.236 Angstrom respectively. The epitope SPLWALRVILAAGIQ from VP24 protein bound with the binding energy of −5.6 kcal/mol (Fig. 2b). It forms a single hydrogen bond with the residue ASN82 having a bond length of 2.056 Angstrom. The selected MHC II epitope from the control antigen, GLIYNRMGTVTTEVA bound to the binding groove with the binding energy of −5.2 kcal/mol and formed four hydrogen bonds with GLU55, SER53, ASN32 and GLN9. The lengths of the bonds are 1.987, 1.926, 1.967 and 2.157 Angstrom respectively (Fig. 3b).

Discussion

This study was focused on the in silico prediction of peptides from two proteins from the Ebola virus in the light of the recent outbreak. Envelope Glycoprotein (GP2) and Viral protein 24 (VP24) were selected as they have been shown to play immensely important role in the viral infection and evasion of the host immune system. Since the virus has mutated multiple times since the first known outbreak, 29 different sequences of GP2 and 19 different sequences of VP24 were downloaded. After performing multiple alignments of all the sequences each protein, 11 and 10 conserved regions were selected for further analysis. Eight and five sequences respectively from GP2 and VP24 were predicted as potential antigens by the VaxiJen server. These antigenic sequences formed the basis of all further analysis.

The major histocompatibilty complex (MHC) is a highly polymorphic group of cell-surface receptors found on all the cells of the body. MHC is categorized in two types: MHC I and II. MHC I is found on all nucleated cells of the body while MHC II is expressed only by APCs. Their role is to present short peptide sequences both from self-proteins and foreign proteins to other elements of the immune system (T Cells) and hence play perhaps the most important role in the training of the immune system. The peptides which are expressed on MHCs are called T Cell epitopes. The antigenic sequences selected previously were used to predict MHC I and II binding epitopes. GP2 yielded 76 unique epitopes which exhibited binding to 12 unique MHC I alleles and 72 unique epitopes for 36 unique MHC II alleles. 19 unique epitopes were found from VP24 for 12 unique MHC I alleles and 40 unique epitopes were found for 30 unique MHC II alleles.

MHC I epitopes predicted above were subjected to population coverage analysis as not all the MHC alleles are expressed in every population. This is necessary to select proper peptides for rational vaccine design since the peptides reacting with the most expressed MHC alleles in a target population will be the most appropriate. The target populations (North Africa, South West Asia, South East Asia and Sub Saharan Africa) selected for analysis were based on all of the outbreaks of Ebola virus since its discovery. The analysis revealed that FLYDRLAST epitope from GP2 reacted to HLA-A*0201, which is the most expressed human MHC I allele in North African, South West Asian and Sub Saharan African populations and LFLRATTEL epitope from the same protein reacted with HLA-A*2402, the most expressed allele in the South East Asian Population. From VP24, NYNGLLSSI epitope reacted with both HLA-A*0201 and HLA-A*2402 in all the target populations.

In addition to the TH Cell response mediated by MHC II, B Cells also play a role in humoral immunity. The peptide regions which bind to B cell receptors are called B cell epitopes. All the conserved sequences were used to perform B cell epitope prediction. Four conserved peptides from GP2 and 2 from VP24 were found to contain B cell epitopes.

The interaction of GP2 and VP24 with the human immune system is already established in various studies. The principle interaction in form of MHC I and II presentation was studied by using molecular docking to analyze the structural binding between MHC alleles and predicted peptides. Autodock vina showed that FLYDRLAST and LFLRATTEL from GP2 fit into the binding groove of with HLA-A2 (MHC I) with binding energies of −7.8 and −8.5 kcal/mol while NYNGLLSSI from VP24 fit into the binding groove of the same MHC molecule with binding energy of −5.6 kcal/mol. The MHC II binding groove is open from both ends unlike the MHC I binding groove which is closed. Therefore, longer peptides (up to about 15 residues long) can fit into this groove. Docking with vina showed that the peptide EGAFFLYDRLASTVI from GP2 fit into the binding groove of HLA-DR1 (MHC II) with the binding energy of −6.2 kcal/mol and SPLWALRVILAAGIQ from VP24 fit into the binding groove of the same molecule with the binding energy of −5.6 kcal/mol. These energies along with the formation of hydrogen bonds show that the selected epitopes can be used as a basis for a novel peptide based vaccine against most of the known strains of Ebola virus. And possibly against newly emerging strains too because the basis of this study was the conservation of protein sequences in various strains.

In this study, we have concentrated on the predicted peptides which can be used as vaccine candidates. Further studies will concentrate on the delivery mechanisms including various adjuvants (Freund’s adjuvant, liposomes, virosomes etc.) and the simulation of interactions with the immune system as a complete vaccine system. Development of polytopic vaccines is a also a valid strategy which takes advantage of linear peptide sequences.

Conclusion

The world is now the habitat of more than seven billion people. With the advent of medical technology, new kinds of diseases are also emerging along with new viruses. Developing world, in particular, is more affected by these sorts of diseases. Diseases which have earlier been recognized as zoonotic are now spreading from human to human. However, medical science has always tried to cope with the problems with the pace of replicating disease. New technologies involving high performance computing for “in silico” design of drugs and vaccines cut the time required to do so by a large extent. Bioinformatics has played an immensely important role in the diverse areas of life sciences. From the basic study of macromolecule sequences and structures to the application of machine-learning algorithms to simulate complete biological systems, bioinformatics is the central pillar of modern life science research. Almost all of the bioinformatics software is either available for free to use or the algorithms are in public domain and can be implemented in any programming language. This study focused on two things. Firstly the importance of bioinformatics and free software in the field of life science research but secondly and most importantly, it focused on the fact that there is a great need to apply bioinformatics to world-wide problems like the recent Ebola virus outbreak to provide with lifesaving solutions (diagnostics, drugs and vaccines) in a short amount of time to maximum people at the lowest possible costs.