"BIOINFORMATICS - An Overview"

Computers and information technology have become indispensable tools for most of us. This is particularly true in biological research, where scientists increasingly apply information technology to biological problems. The recent flood of data from genome sequences and functional genomics has given rise to new field, Bioinformatics, which combines elements of biology and computer science. Computers are used to gather, store, analyze and integrate biological and genetic information which can then be applied to gene-based drug discovery and development.

WHAT IS BIOINFORMATICS?

Bioinformatics is an interdisciplinary field that develops methods and software tools for understanding biological data. As an interdisciplinary field of science, bioinformatics combines computer science, statistics, mathematics, and engineering to study and process biological data.

The science of bioinformatics actually develops algorithms and biological software of computer to analyze and record the data related to biology for example the data of genes, proteins, drug ingredients and metabolic pathways. As biological data is always in raw form and there is a need of certain storage house in which the data can be stored, organized and manipulated. Bioinformatics provides central, globally accessible databases that enable scientists to submit, search and analyse information. It offers analysis software for data studies and comparisons and provides tools for modelling, visualising, exploring and interpreting data.

Nevertheless, bioinformatics has been defined in many different ways, since practitioners do not always agree upon the scope of its use within the biological and computer sciences, but it is always considered a combination of both sciences, along with other contributing disciplines.

GOALS OF BIOINFORMATICS

The primary goal of bioinformatics is to increase the understanding of biological processes. What sets it apart from other approaches, however, is its focus on developing and applying computationally intensive techniques to achieve this goal. Examples include: pattern recognition, data mining, machine learning algorithms, and visualization. Major research efforts in the field include sequence alignment, gene finding, genome assembly, drug design, drug discovery, protein structure alignment, protein structure prediction, prediction of gene expression and protein–protein interactions, genome-wide association studies, and the modeling of evolution.

APPLICATIONS OF BIOINFORMATICS

Bioinformatics joins mathematics, statistics, and computer science and information technology to solve complex biological problems. These problems are usually the molecular level which cannot be solved by other means. This interesting field of science has many applications and research areas where it can be applied including :

• Molecular medicine
• Personalised medicine
• Preventative medicine
• Gene therapy
• Drug development
• Microbial genome applications
• Waste cleanup
• Climate change Studies
• Alternative energy sources
• Biotechnology
• Antibiotic resistance
• Forensic analysis of microbes
• The reality of bioweapon creation
• Evolutionary studies
• Crop improvement
• Insect resistance
• Development of Drought resistance varieties
• Veterinary Science
• Comparative Studies

BIOINFORMATICS SOFTWARES

Software tools for bioinformatics range from simple command-line tools, to more complex graphical programs and standalone web-services available from various bioinformatics companies or public institutions. They are designed for extracting the meaningful information from the mass of molecular biology / biological databases & to carry out sequence or structural analysis. Some examples of bioinformatics tools are :

1. BLAST:

BLAST ( Basic Local Alignment Search Tool) comes under the category of homology and similarity tools. It is a set of search programs designed for the Windows platform and is used to perform fast similarity searches regardless of whether the query is for protein or DNA. Comparison of nucleotide sequences in a database can be performed. Also a protein database can be searched to find a match against the queried protein sequence.

2. FASTA:

FAST homology search A ll sequences. An alignment program for protein sequences created by Pearsin and Lipman in 1988. The program is one of the many heuristic algorithms proposed to speed up sequence comparison. The basic idea is to add a fast prescreen step to locate the highly matching segments between two sequences, and then extend these matching segments to local alignments using more rigorous algorithms such as Smith-Waterman.

3. EMBOSS:

EMBOSS (European Molecular Biology Open Software Suite) is a software-analysis package. It can work with data in a range of formats and also retrieve sequence data transparently from the Web. Extensive libraries are also provided with this package, allowing other scientists to release their software as open source. It provides a set of sequence-analysis programs, and also supports all UNIX platforms.

4. Clustalw:

It is a fully automated sequence alignment tool for DNA and protein sequences. It returns the best match over a total length of input sequences, be it a protein or a nucleic acid.

5. RasMol:

It is a powerful research tool to display the structure of DNA, proteins, and smaller molecules. Protein Explorer, a derivative of RasMol, is an easier to use program.

6. PROSPECT:

PROSPECT (PROtein Structure Prediction and Evaluation Computer ToolKit) is a protein-structure prediction system that employs a computational technique called protein threading to construct a protein's 3-D model.

7. PatternHunter :

PatternHunter, based on Java, can identify all approximate repeats in a complete genome in a short time using little memory on a desktop computer. Its features are its advanced patented algorithm and data structures, and the java language used to create it. The Java language version of PatternHunter is just 40 KB, only 1% the size of Blast, while offering a large portion of its functionality.

8. COPIA :

COPIA (COnsensus Pattern Identification and Analysis) is a protein structure analysis tool for discovering motifs (conserved regions) in a family of protein sequences. Such motifs can be then used to determine membership to the family for new protein sequences, predict secondary and tertiary structure and function of proteins and study evolution history of the sequences

Potential Drug Target Database

PDTD is a dual function database that associates an informatics database to a structural database of known and potential drug targets. PDTD is a comprehensive, web-accessible database of drug targets, and focuses on those drug targets with known 3D-structures. PDTD contains 1207 entries covering 841 known and potential drug targets with structures from the Protein Data Bank (PDB).
Drug targets of PDTD were categorized into 15 and 13 types according to two criteria: therapeutic areas and biochemical criteria.
The database supports extensive searching function using PDB ID, target name and category, related disease.Each record of drug target was prudently annotated by hyperlinks to other databases, such as DrugBank, TTD, ExPASy Proteomics Server and KEGG etc.
In conclusion, PDTD serves as the cornerstone for TarFisDock to identify the potential binding targets in-silico.

Protein Database

     The Protein Data Bank (PDB) is a repository for the three-dimensional structural data of large biological molecules, such as proteins and nucleic acids. This resource is about the 3D shapes of proteins, nucleic acids, and complex assemblies that helps students and researchers understand all aspects of biomedicine and agriculture, from protein synthesis to health and disease. 

          Protein Data Bank gives knowledge that can be used to help deduce a structure's role in human health and disease and in drug development. Most major scientific journals, and some funding agencies, now require scientists to submit their structure data to the PDB. One of the most common example, is the RSCB Protein Data Bank.

The result on E. Coli Replication Terminator Protein Complexed with DNA-locked form.

Drug Bank

       The Drug Bank database is a unique bioinformatics and resource that combines detailed drug data with comprehensive drug target information such as sequence, structure, and pathway. Because of its broad scope, comprehensive referencing and unusually detailed data descriptions, Drug Bank is more akin to a drug encyclopedia than a drug database.

       Drug Bank is widely used by the drug industry, medicinal chemists, pharmacists, physicians, students and the general public. Its extensive drug and drug-target data has enabled the discovery and repurposing of a number of existing drugs to treat rare and newly identified illnesses.

       Currently, the database contains 7759 drug entries including 1600 FDA-approved small molecule drugs, 160 FDA-approved biotech drugs, 89 nutraceuticals and over 6000 experimental drugs. Additionally, 4282 non-redundant protein sequences are linked to these drug entries. Each DrugCard entry contains more than 200 data fields with half of the information being devoted to drug/chemical data and the other half devoted to drug target or protein data.

                            

Results on Acetaminophen.

Therapeutically Relevant Multiple Pathways Database

         The Therapeutically Relevant Multiple Pathways Database is designed to provide information about such multiple pathways and related therpaeutic targets described in the literatures, the targeted disease conditions, and the corresponding drugs/ligands directed at each of these targets. This database currently contains 11 entries of multiple pathways, 97 entries of individual pathways, 120 targets covering 72 disease conditions along with 120 sets of drugs directed at each of these targets. Each entry can be retrieved through multiple methods including multiple pathway name, individual pathway name and disease name. Additional information provided include protein name, synonyms, Swissprot AC number, species, gene name and location, protein sequence (AASEQ) and gene sequence (NTSEQ) as well as potential therapeutic implications while applicable. Cross-links to other databases are provided which include Genecard, GDB, Locuslink, NCBI, KEGG, OMIM, SwissProt to facilitate the access of more detailed information about various aspects of the particular target or non-target protein.

Drug Adverse Reaction Target

     Drug Adverse Reaction Target (DART) is a database for facilitating the search for drug adverse reaction target. It contains information about known drug adverse reaction targets, functions and properties.

     An adverse drug reaction (ADR) often results from interaction of a drug or its metabolites with specific protein targets important in normal cellular function. Knowledge about these targets is both important in facilitating the study of the mechanisms of ADRs and in new drug discovery.

     It is also useful in the development and testing of rational drug design and safety evaluation tools. The Drug Adverse Reaction Database (DART) is intended to provide comprehensive information about adverse effect targets of drugs described in the literature.

     Moreover, proteins involved in adverse effect targets of chemicals not yet confirmed as ADR targets are also included as potential targets. This database gives physiological function of each target, binding drugs/agonists/antagonists/activators/inhibitors, IC values of the inhibitors, corresponding adverse effects, and type of ADR induced by drug binding to a target.

     Cross-links to other databases are also introduced to facilitate the access of information about the sequence, 3-dimensional structure, function, and nomenclature of each target along with drug/ligand binding properties, and related literature. Each entry can be retrieved through multiple search methods including target name, target physiological function, adverse effect, ligand name, and biological pathways.

Therapeutic Target Database

This website allows you to access the databases according to 

Therapeutic Target Database search method.

Therapeutic Target Database is a database to provide information about the known and explored therapeutic protein and nucleic acid targets, the targeted disease, pathway information and the corresponding drugs directed at each of these targets. The link that may be referred to be accessed on these type of findings is http://bidd.nus.edu.sg/group/cjttd

      



Below is the illustration on the search methods available in order to search the databases from TTD after simply clicking the "Click Here To Enter TTD" icon . 

By using the TTD, we can search through many different methods included in the search box.

        

An example shown is search using the search target by disease named "cataract".

It would show the results including the drug availability according to the search keywords.

Below is the example of the detailed information that is available for reference based on the TTD search method. It would also give links and explanation on where the information is taken from or being referred to.

HumanCyc Database

           HumanCyc is a bioinformatics database that provides an encyclopedic reference on human metabolic pathways and the human genome. It provides a zoomable human metabolic map diagram, and it has been used to generate a steady-state quantitative model of human metabolism.
           By presenting metabolic pathways as an organising framework for the human genome, HumanCyc provides the user with an extended dimension for functional analysis of Homo Sapiens at the genomic level. For example, HumanCyc.org has tools for analysis pathway of human biosynthesis.

About Us

Hi, Welcome to our blog !

We are the first year students in Bachelor of Pharmacy from MSU.

Now, introducing you the Three Hot Stuffs of MSU, from left is Muhammad Jibril bin Muhammad Faiz, Amirul Naim bin Hishamsul Anuar and Muhammmad Hafizi bin Jasmi.

The three of us is actually have been together for one year in MSU during the Foundation year.

So, we have already know each other before we continue our study in BPH this year. 

And we usually hang out together everyday.