Bioinformatics

Bioinformatics
DR. SIDDHI UPADHYAY (M.PHARM, PH.D)
H.O.D. AND ASSOCIATE PROFESSOR
DEPARTMENT OF PHARMACOGNOSY AND PHYTOCHEMISTRY
SIGMA INSTITUTE OF PHARMACY, BAKROL, WAGHODIA, VADODARA

CONTENT
 Introduction
 Objective of Bioinformatics
 Bioinformatics Databases
 Concept of Bioinformatics
 Impact of Bioinformatics in Vaccine Discovery

Introduction – 1/2
Bioinformatics is an interdisciplinary field that develops
methods and software tools for understanding biological
data.
As an interdisciplinary field of science, bioinformatics
combines biology, computer science, information
engineering, mathematics and statistics to analyze and
interpret biological data.
Bioinformatics has been used for in silico (performed on
computer or via computer simulation) analyses of biological
queries using mathematical and statistical techniques.

Introduction – 2/2
Applications of Bioinformatics:
1. Analyze the biological processes.
2. Aids in improving drug discovery.
3. Help in developing new target drug.
4. Study and research.

Objectives of Bioinformatics – 1/2
The field of bioinformatics has three main objectives
1. To organize vast sreams of molecular biology data in an efficient
manner
2. To develop tools that aid in the analysis of such data
3. To interpret the results accurately and meaningfully

Objectives of Bioinformatics – 2/2
Need for Bioinformatics
The need for Bioinformatics has arisen from the recent explosion of
publicly available genomic information, such as that resulting from
the Human Genome Project.
Gain a better understanding of gene analysis, taxonomy, and
evolution.
To work efficiently on rational drug design and reduce drug
development duration/time.

Bioinformatics Databases – 1/4
Databases are essential for bioinformatics research and applications.
Many databases exist, covering various information types: for
example, DNA and protein sequences, molecular structures,
phenotype and biodiversity.
Databases may contain empirical data (obtained directly from
experiments), predicted data (obtained from analysis), or, most
commonly, both. They may be specific to a particular organism,
pathway or molecule of interest.
Alternatively, they can incorporate data compiled from multiple
other databases. These databases vary in their format, access
mechanism, and whether they are public or not.

Some of the most commonly used databases are listed below:
Used in biological sequence analysis: Genbank, UniProt
Used in structure analysis: Protein Data Bank (PDB)
Used in finding Protein Families: InterPro, Pfam
Used for Next Generation Sequencing: Sequence Read Archive
Used in Network Analysis: Metabolic Pathway Databases (KEGG, BioCyc),
Interaction Analysis Databases, Functional Networks
Used in design of synthetic genetic circuits: GenoCAD
Used in calculateion of drug DNA interaction: PREDDICTA

Classification of Bioinformatics Databases:
Databases can be classified on the basis of:
a) Data type,
b) Data source,
c) Database design,
d) Special category.

Concept of Bioinformatics – 1/5
Simply, bioinformatics is the science of storing,
retrieving and analysing large amounts of
biological information.
It is a highly interdisciplinary field involving
many different types of specialists, including
biologists, molecular life scientists, computer
scientists and mathematicians.

The term bioinformatics was coined by Paulien Hogeweg
and Ben Hesper to describe "the study of informatic
processes in biotic systems" and it found early use when the
first biological sequence data began to be shared.
Whilst the initial analysis methods are still fundamental to
many large-scale experiments in the molecular life sciences,
nowadays bioinformatics is considered to be a much
broader discipline, encompassing modelling and image
analysis in addition to the classical methods used for
comparison of linear sequences or three-dimensional
structures.

Traditionally, bioinformatics was used to
describe the science of storing and analysing
biomolecular sequence data, but the term is
now used much more broadly, encompassing
computational structural biology, chemical
biology and systems biology (both data
integration and the modelling of systems).

The molecular life sciences have become increasingly data driven by
and reliant on data sharing through open-access databases.
This is as true of the applied sciences as it is of fundamental research.
Furthermore, it is not necessary to be a bioinformatician to make use of
bioinformatics databases, methods and tools.
However, as the generation of large data-sets becomes more and more
central to biomedical research, it’s becoming increasingly necessary for
every molecular life scientist to understand what can (and, importantly,
what cannot) be achieved using bioinformatics, and to be able to work
with bioinformatics experts to design, analyse and interpret their
experiments.

Impact of Bioinformatics in Vaccine
Discovery – 1/5
Vaccines are the pharmaceutical products that offer the
best cost‐benefit ratio in the prevention or treatment of
diseases.
In that a vaccine is a pharmaceutical product, vaccine
development and production are costly and it takes years
for this to be accomplished.
Several approaches have been applied to reduce the times
and costs of vaccine development, mainly focusing on the
selection of appropriate antigens or antigenic structures,
carriers, and adjuvants.

Discovery – 2/5
One of these approaches is the incorporation of bioinformatics
methods and analyses into vaccine development.
Reverse vaccinology, immunoinformatics, and structural vaccinology
are described and addressed in the design and development of
specific vaccines against infectious diseases caused by bacteria,
viruses, and parasites.
These include some emerging or re‐emerging infectious diseases, as
well as therapeutic vaccines to fight cancer, allergies, and substance
abuse, which have been facilitated and improved by using
bioinformatics tools or which are under development based on
bioinformatics strategies.

Discovery – 3/5
The success of vaccination is reflected in its worldwide impact
by improving human and veterinary health and life expectancy.
It has been asserted that vaccination, as well as clean water, has
had such a major effect on mortality reduction and population
growth.
In addition to the invaluable role of traditional vaccines to
prevent diseases, the society has observed remarkable scientific
and technological progress since the last century in the
improvement of these vaccines and the generation of new ones.

Discovery – 4/5
This has been possible by the fusion of computational technologies with
the application of recombinant DNA technology, the fast growth of
biological and genomic information in database banks, and the possibility
of accelerated and massive sequencing of complete genomes.
This has aided in expanding the concept and application of vaccines
beyond their traditional immunoprophylactic function of preventing
infectious diseases, and also serving as therapeutic products capable of
modifying the evolution of a disease and even cure it.
At present, there are many alternative strategies to design and develop
effective and safe new generation vaccines, based on bioinformatics
approaches through reverse vaccinology, immunoinformatics, and
structural vaccinology.

Discovery – 5/5
Reverse vaccinology
Reverse vaccinology is a methodology that uses bioinformatics tools for the identification of structures
from bacteria, virus, parasites, cancer cells, or allergens that could induce an immune response capable of
protecting against a specific disease.
Immunoinformatics
The immunological system can be classified as cellular or humoral and, depending on the disease, it can be
induced the expected immune response. If a vaccine that induces a cellular response is needed, for
example a tuberculosis vaccine or a parasite vaccine against leishmaniasis, the software must search for
antigens that can be recognized by the major histocompatibility complex (MHC) molecules present in T
lymphocytes.
Structural vaccinology
Structural vaccinology focuses on the conformational features of macromolecules, mainly proteins that
make them good candidate antigens. This approach to vaccine design has been used mainly to select or
design peptide‐based vaccines or cross‐reactive antigens with the capability of generating immunity
against different antigenically divergent pathogens.

Bioinformatics

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