Bioinformatics Applications in Vaccine Design

This assignment delves into the crucial applications of bioinformatics in modern vaccine design. It examines various bioinformatic tools and databases used for analyzing viral sequences, identifying conserved regions, and predicting potential vaccine candidates. The assignment emphasizes the importance of bioinformatics in accelerating the process of developing effective vaccines against infectious diseases.

Running Head: APPLICATIONS OF BIOINFORMATICS IN BIOTECHNOLOGY AND RESEARCH
Applications of bioinformatics in biotechnology and research.
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1APPLICATIONS OF BIOINFORMATICS IN BIOTECHNOLOGY AND RESEARCH
Table of contents.
The general field of bioinformatics ……………………………………….…… 1 Types
of data in bioinformatics ……………………………………..………… 4
Applications of bioinformatics ………………………………………………. 7
Vaccine Discovery …………………………………………..…… 7
Pathogenesis and bioinformatics……..……………..…….…… 9
Bioinformatics and medicine …………………………………………. 10
Conclusion ………………………………………………………………………… 11

2APPLICATIONS OF BIOINFORMATICS IN BIOTECHNOLOGY AND RESEARCH
Introduction to the general field of bioinformatics.
Bioinformatics tools are important in fundamental research on the evolutionary theories
and practical instances of the protein design. They are used in biotechnology and other aspects of
biological research. Various approaches and algorithms that are used in such studies include;
alignments of the structure and sequences, prediction of the secondary structure, classification of
proteins and progress of protein expression in the cell cycle (Felix et al., 2005). In this essay, we
shall discuss the uses of bioinformatics in biotechnology, biological sciences and medical
research critically examining the general field of bioinformatics, types of data involved in
bioinformatics and the applications of bioinformatics in the scientific process.
Rana (2012) argues that genome sequencing and the analysis of the X-ray structure have
led to enormous amounts of structures and sequences of multiple proteins into the scientific
community. The information obtained from such analysis can be used in biological and medical
research effectively, if one can interpret the information they provide appropriately (p.10). Two
types of computational techniques can be used in the analysis of such data these include
simulations of the full atoms in molecular dynamics or the bioinformatics approach (Rana, 2012,
p 11).
Bioinformatics is a field in biological sciences that involves statistical analysis of the
structure and sequences of proteins. Moreover, it aids in the annotation of the genome,
understanding its function and predict structures. Nevertheless, the process is possible when the
protein sequence information is available. Bioinformatics has brought a major revolution in
biological sciences with powerful tools that provide vast information. They are the most complex

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and powerful tools in biological sciences presently. Moleculardynamics and molecular modeling
simulations study the folding and functions of proteins (Rana, 2012, p.12).
According to the National Institute of Health, bioinformatics is involved in research,
development and application of tools in computation to widen the medical, behavioral and
biological data. In addition to that, it helps to acquire, store, organize and interpret information.
Bioinformatics has been used in the Human Genome Project, which has attracted much interest
from researchers and facilitated the analysis of large amounts of bio data. The data needs to be
analyzed due to the advances made in molecular biology techniques (Kumar, 2015, p.2).
Rana (2012) further illustrates that bioinformatics has led to important discoveries in
drugs and medicine, plant sciences biology furthermore, it has helped pharmaceutical companies
to save money, time and management of large biological data. In addition to that, its aims
include organizing data for researchers to gain easy access to information, to develop data
analysis tools and interpret information in a meaningful way. Moreover, bioinformatics provides
available tools to analyze data and interpret results (p.14).
Research areas in bioinformatics include genomics, proteomics, and computer aided drug
design. In addition to that, research areas further include biological databases, biological data
mining, microarray informatics, molecular phylogenetics, (study of an organisms at the
molecular level in order to gather information on phylogenetic relationships of organisms) and
agro informatics (agricultural informatics that deal with plant research) (Rana, 2012, pp. 13- 18).

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Types of Data in Bioinformatics.
` Kraulis (2001) emphasizes on the increasing nature and availability of biological data; a
phenomenon has necessitated creation of databases whose sole purpose is to collect data,
organize it in a form that is meaningful and ensures easy interpretation (par. 1). Databases have
been classified into different forms to maintain order within the scientific process, improve
accessibility to information and reduce repetitions. Moreover, in order to ease the access to data,
it is important to first have the needed information and seek it from the appropriate database
(Kavitha, 2012).
Databases are classified according to the data that they accommodate. The types of data
include one, biomolecule sequences, proteins and nucleic acids, for example, EMBL, DDJB,
Genebank, PIR and Swiss-Prot. Two, bio-molecular structures with examples such as PDB.
Thirdly, we have bibliographies or scientific literatures and their examples include Scopus and
PubMed, these are search engines and some are free while others require subscription to access
content. In addition to that, we have gene expression profiles, genetic disorders and whole
genome sequences (Kavitha, 2012).
The data or information has sources that are categorized into primary databases,
secondary databases, composite databases and integrated databases. Primary databases have
molecular data presented in its initial form. Examples of primary databases are GenBank, for
sequences in nucleic acids, Protein Data Bank (PDB) for molecular structures, PIR (Protein
Information Resource) and SWISS-PROT for protein sequences. They contain combinations of
data such as gene sequences from mRNA or genomic DNA, genome sequences, chromosome
sequences, annotated entries and partial or complete entries (Welcome Genome Campus, 2017).

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Secondary databases have information derived from primary data analysis and it is more
useful and relevant. Furthermore, the information is structured to meet specific articulated
requirements. Examples of secondary databases include UniGene and Eukaryotic Promoter
Databases, which are secondary databases that are sequence based. The evolutionary and
structural relationships between the known structures of proteins is described by SCOP
(Structural Classification of Proteins).The hierarchical classification of structures in proteins is
included in CATH (Class, Architecture, Topology, Homology) (Welcome Genome Campus,
2017).
Composite databases are repertoires of secondary data and they are easier to use since
they allow the user to access all information that is relevant from one source instead of
connecting to multiple resources. The NCBI database (National Centre for Biotechnology
Information) is one best composite databases. In addition to that, it includes many primary and
secondary databases such as PubMed, Genbank, and OMIM. NCBI is a free online database for
accessing gene sequences of phyla and species. The database includes gene alleles and
mutations, gene sequences, protein sequences and genome pathways (Lesk, 2008).
Finally, integrated databases have data from different organisms that are related. They
are important for studies involving genomic relationships in organisms, they also illustrate
relations in evolution within organisms. These types of investigations are important in
phylogenetics since genes that allow for expression of traits of economic value can be identified
in plants. For example, Arabidopsis thaliana integrated databases provide genome and
transcriptome sequence data linking a Brassica species of economic value and an organism that
acts as a model (Lesk, 2008).

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Furthermore, there are other remarkable types of databases such as SGN (Sol Genomics
Networks) for organisms such as potato, tomato, eggplant and the pletunia. Legume Base for
Glycine max and Lotus japonicas. Bean genes for Vigna species and Phaseoulus. Gramene
databases for rice, maize, barley, wheat, oats and foxtail. Plant Transcript Assemblies Databases
for several plant species. Aphid Base databases for several aphid species and SYSTOMONAS
databases for biotechnology and the infection of Pseudomonads .Human Ageing Genomic
Resources (HAGR) for the genetics and biology of aging in humans. FLYMINE databases for
Anopheles and Drosophila genomics (Seung et al., 2006).
Several databases can be merged on the basis of an organism's taxonomic identity. The
merger of databases leads to formation of integrated databases. Presently, work on the analysis of
the genome and transcriptome of many species has started. Consequently, the work has
developed more databases that are organ specific. They include Chlamydomonas Center algae
for green alga, Medicago.org for Medicago truncatula, Soybase for soybean, Oryzabase for
Oryza species (rice), FLYBASE for Drosophila and OMIM for genetic disorders. They collect
data obtained using various techniques used in studying plant systems which include linkage
maps, microarray data, transcriptome and genome sequencing (Seung et al., 2006).
Many of these databases are obtained through websites that organize the data in a
way that a user can easily access it online. In addition to that, same data can be downloaded from
websites in a various formats. The formats include sequence data, text links and protein
structure. These formats can be found from given sources such as OMIM and PubMed that
provide text formats, GenBank that provides sequence data in terms of DNA, and Uniprot in
terms of protein and finally, protein structure are provided by CATH, SCOP and PDB((Lesk,
2008).

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Applications of bio-informatics.
Vaccine discovery
The availability of genomic data, computing resources, technology, immunogenetics, and
the better understanding of the immune process has led to vaccine research (Shanju &
Shangeetha, 2013). The science of reverse vaccinology and rational design of vaccines are the
new indicators of vaccine development in future, the methods have been used to study peptide
vaccines. The protein antigen in a viral genome that brings forth an immune response is scanned
and then synthesized to a peptide vaccine; this is used in development of vaccines against
various viruses such as coronavirus and influenza (Smith, 2003).
Gregory (2010) states that the recent advancement in technology and bioinformatics
enables computer-based approach in the development of vaccines. Over the years, peptide
vaccines have promised to be effective in humans. Furthermore, advances made in proteomics
have resulted in vaccinomics and reverse vaccinology as new techniques of developing vaccines
(p. 510). Advances in technological and scientific tools have resulted in stronger inhibitors such
as AIDS drugs for example Viracept, Aegenerase from structure based design approaches, and
Relenza inhibitors made for influenza (Nandy & Subhash, 2014).
American Biopharmaceutical companies (2013), state that peptide vaccines have been
showing good promises in relation to tertiary cancers and other diseases and there is a good
response from cancer patients in regards to improved immunity (p.1). Furthermore, there is a
high rising interest in peptide vaccines; the process of their design, determining the desired
proteins and the protein sequences involved, this requires application of bioinformatics. Reliable
and good results need the approval of molecular level data for every virus used in the vaccines,

8APPLICATIONS OF BIOINFORMATICS IN BIOTECHNOLOGY AND RESEARCH
and a reliable technique that will be used in data analysis to identify the protein sequences of
interests for the purpose of vaccine development (Danylo et al. 2011).
Nandy & Subhash (2014) investigated the use of bioinformatics in designing the human
corona virus peptide vaccine. This virus (HCoV) causes infections on the upper respiratory tract
and early in the century it led to a SARS outbreak (p.4), The HCoV protein had 56 strains that
were presented to the Vaxi Jen 2.0 server. The protein with the highest antigenicity index was
identified for analysis. The prediction of epitopes in T cell response was done. Five peptides
were selected from the Net CTL 1.2 Server that predicted the presence of CTL (Cytotoxic T
Lymphocytes) epitopes in the protein sequences (p.5).
The epitope that was identified had an amino acid sequence of KSSTGFVYF and it
interacted with several MHC 1 alleles at a higher affinity .The conservancy of B cell epitope was
determined from IEBD server and its allergenicity obtained from AllerHunter tool .The epitope
had a conservancy of 64.29% and a low allergenicity result. The selected peptide underwent a
molecular docking analysis and the peptide was HLA-B*15: 01 which showed a good binding
(Nandy & Subhash, 2014, p.6).
Kolaskar and Tangaokar antigenicity prediction method was used in searching for the B-
cell epitope and seven regions with a high antigen scores were shown but were later reduced to
three after determination of solvent accessibility by the IEDB Analysis resource. An analysis was
further done with linear B cell epitopes server to analyze the epitopes of the B cell and after the
analysis, the peptide GPSSQPY was concluded to have the ability to induce an immune response
when used with the B cell epitopes .Therefore the vaccines could now be formulated using
protein peptides information that was available ( Nandy & Subhash., 2014, p.6).

9APPLICATIONS OF BIOINFORMATICS IN BIOTECHNOLOGY AND RESEARCH
Pathogenesis and bioinformatics.
Pathogenesis is a study of biological mechanisms that cause disease state in the body. It
also describes the development and the origin of a disease and whether the disease is acute,
chronic or recurrent. The mechanisms of pathogenesis are set by the course of the disease and the
disease can be prevented if the underlying causes are controlled. Bioinformatics can be used to
determine pathological links between the diseases and their causes and if the cause can be
determined then the disease can be controlled by looking at the molecular pathology signatures
of the disease ( Zhumar & Malik, 2003, p.47).
Pancreatic cancer is regularly a lethal disease and in its early stages, it can be difficult to
diagnose .Bioinformatics approach can be used to analyze the pathogenesis of this disease by
identifying causal genome which might lead to prevention of occurrence of the disease. In
addition to that, bioinformatics can be used to investigate the mechanisms of disease and
recognize the new and present disease targets, therefore assist in therapy (Zhao et al., 2014).The
following is an outline on the use of biotechnology in pathogenesis.
The data GSE 16515 has 16 normal samples and 36 tumor samples available from the
GEO database. This is a database that stores and distributes freely next generation microarray
and high output genomic sets of data from a wide array of biological subjects of diseases.
LIMMA Package and Robust Multichip Averaging are used in screening out Differentially
Expressed Genes (DEGs). Furthermore, gene ontology and analysis on the pathway enrichment
are conducted the genes, which is followed by protein –protein interaction (PPI) network
connection, this is done by the Cytoscape and STRING. ClusterONE is used to perform module
analysis (Zhao et al., 2014).

10APPLICATIONS OF BIOINFORMATICS IN BIOTECHNOLOGY AND RESEARCH
Text mining based on the DEGs is conducted based on Pub Med. 93 downregulated and
274 upregulated genes are identified as the prominent DEGs, and they are found to exist
significantly in the extracellular region and EM receptor pathways. In addition to this, no
modules were screened in down regulated PPI networks while five were screened out from the
up-regulated networks. The down-regulated genes included INS, FGF, and LAPP while up
regulated genes included MET, MIA and CEA. CAMS had the highest number of inferences
during the text mining analysis. The findings demonstrated that in conclusion, up and down
regulated genes had an important role to play in the development of pancreatic cancers and this
are the new targets for therapy of the disease (Zhao et al., 2014).
Bioinformatics in medicines
Bioinformatics has impacted the medical field as it helps in diagnosis of diseases and
furthermore it helps physicians use the information it produces to develop strategies for therapy.
According to Bala (2014), bioinformatics can be used in the diagnosis of clinical conditions for
example a patients might present to a physicians with a form of hemophilia that is genetic. They
might be unsure of the disease symptoms but only have a clue from the history and information
given about an early occurrence of the disease in the family. The following is an outline on how
the physician will use bioinformatics to diagnose the disease.
The physician will use the Web to obtain information about the disease by clicking on
the OMIM database, which provides information relating to various genetic disorders. A search
can be put on diabetes which reveals many diseases such as the Von Willerband disease .In
addition to the search gives an important information about the patient which states that the
patient has a low level of anti hemophilic globulin in the disease(factor VIII). Furthermore, when

11APPLICATIONS OF BIOINFORMATICS IN BIOTECHNOLOGY AND RESEARCH
factor VIII is searched on the protein sequence database it will lead to a match that encodes the
factor VIII with an incomplete DNA and the equivalent protein sequence. In this study, the gene
is linked to its protein and DNA sequences (Bal, 2005, p. 121).
Furthermore, it is also linked to a reference set in the MEDLINE database. According to
the MEDLINE literature database, there is an earlier research article which explains the
association of hemophilia with factor VIII. Detailed information from Protein Information
Resources SWISS-PROT database is found on the protein sequence link. A link to Protein
DataBank provides information regarding to the crystal structure of the protein in a SWISS
PROT database (Bal, 2005, p.121).
The genes, nucleotide sequences can now be obtained coupled with records of gene
irregularities by following a DNA sequence link on the GENBANK database. Therefore, the
health physician can use plenty more databases to get information relating to the diseases and
analyze the information, a technique that enables the physician to diagnose treatment and make
further strategies regarding the therapy (Bal, 2005, p.121).
Bioinformatics has emerged as a very important tool for the present day scientist, since
its development, it has shown significant importance. The data is growing tremendously
therefore a need for collecting the data, storing it, managing and further analyzing it so that
researchers can easily access and add more entries. Bioinformatics is a very important tool
especially in drug discovery, biotechnology and medical science. The essay has illustrated a
specific use of bioinformatics in designing vaccines and analyzing the pathogenesis of pancreatic
cancer. Furthermore, it has shown the use of bioinformatics in therapy and diagnosing diseases.

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