Pharmaceutical bioinformatics

Bioinformatics and structure- aided drug design are really part of the same continuum. Bioinformatics offers a means to get to a structure through sequence; while structure- aided drug design offers a means to get to a drug through structure. We plan to combine innovative computational techniques with biochemical and structural expertise to bring bioinformatics and structure- aided drug design even closer together. In particular, we intend to blend computational chemistry with computational biology to create software that will aid protein chemists in understanding, evaluating and predicting the structure, function and activity of medically and industrially important proteins. My laboratory is currently involved in three "bioinformatics" projects. These include: (1) the development of novel methods to identify remote sequence/ structure relationships; (2) the creation of a compact, relational database with advanced bioinformatics functionality; and (3) the development of novel methods for predicting and evaluating protein secondary and tertiary structure.

Molecular modeling:

A technique for the investigation of molecular structures and properties using computational chemistry and graphical visualization techniques in order to provide a plausible three- dimensional representation under a given set of circumstances. IUPAC Medicinal Chemistry

in silico: Literally "in the computer" (as contrasted with "in vitro" (in glass) or "in vivo" (in life). Can be used to screen out compounds which are not druggable.

Mapping and modeling networks and pathwaysThe experimental task of mapping genetic regulatory networks using genetic footprinting and [yeast] two- hybrid techniques is well underway, and the kinetics of these networks is being generated at an astounding rate. ... If the promise of the genome projects and the structural genomics effort is to be fully realized, then predictive simulation methods must be developed to make sense of this emerging experimental data.

There are three bottlenecks in the numerical analysis of biochemical reaction networks. The first is the multiple time scales involved. Since the time between biochemical reactions decreases exponentially with the total probability of a reaction per unit time, the number of computational steps to simulate a unit of biological time increases roughly exponentially as reactions are added to the system or rate constants are increased. The second bottleneck derives from the necessity to collect sufficient statistics from many runs of the Monte- Carlo simulation to predict the phenomenon of interest. The third bottleneck is a practical one of model building and testing: hypothesis exploration, sensitivity analyses, and back calculations, will also be computationally intensive.

Cheminformatics definitions

Mixing of information technology and management to transform data into information and information into knowledge for the intended purpose of making better decisions faster in the arena of drug lead identification and optimization. . In Chemoinformatics there are really only two [primary] questions: 1.) what to test next and 2.) what to make next. The main processes within drug discovery are lead identification, where a lead is something that has activity in the low micromolar range, and lead optimization, which is the process of transforming a lead into a drug candidate.

Increasingly incorporates "compound registration into databases, including library enumeration; access to primary and secondary scientific literature; QSAR Quantitative Structure Activity Relationships) and similar tools for relating activity to structure; physical and chemical property calculations; chemical structure and property databases, chemical library design and analysis; structure- based design and statistical methods. Because these techniques have traditionally been considered the realms of scientists from different disciplines, differences in computer systems and terminology provide a barrier to effective communication. This is probably the single most challenging problem that chemoinformatics must solve.


Many people view chemoinformatics as an extension of chemical information, which is a well established concept covering many areas that employ chemical structures, data storage and computational methods, such as compound registration databases, on- line chemical literature, SAR analysis and molecule- property calculation.

Protein bioinformatics

Structural bioinformaticsInvolves the process of determining a protein's three- dimensional structure using comparative primary sequence alignment, secondary and tertiary structure prediction methods, homology modeling, and crystallographic diffraction pattern analyses. Currently, there is no reliable de novo predictive method for protein 3D- structure determination. Over the past half- century, protein structure has been determined by purifying a protein, crystallizing it, then bombarding it with X-rays. The X-ray diffraction pattern from the bombardment is recorded electronically and analyzed using software that creates a rough draft of the 3D structure. Biological scientists and crystallographers then tweak and manipulate the rough draft considerably. The resulting spatial coordinate file can be examined using modeling- structure software to study the gross and subtle features of the protein's structure.

GEO

In the recent past, microarray technology has been extensively used by the scientific community. Consequently, over the years, there has been a lot of generation of data related to gene expression. This data is scattered and is not easily available for public use. For easing the accessibility to this data, the National Center for Biotechnology Information (NCBI) has formulated the Gene Expression Omnibus or GEO. It is a data repository facility which includes data on gene expression from varied sources.

Microarray probe design parameters

For 25-35 mers

Parameter	Minimum Value	Maximum Value	Default Value	Unit
Probe Length	10	99	30	bases
Probe Length tolerance	0	15	3
Probe Target Tm	40	99	63	°C
Probe Tm Tolerance (+)	0.1	99	5
Hairpin Max ÄG	0.1	99.9	4	Kcal/mol
Self Dimer ÄG	0.1	99.9	7	Kcal/mol
Run/Repeat	2	99	4	bases

For 35-45 mers

Parameter	Minimum Value	Maximum Value	Default Value	Unit
Probe Length	10	99	40	bases
Probe Length tolerance	0	15	3
Probe Target Tm	40	99	70	°C
Probe Tm Tolerance (+)	0.1	99	5
Hairpin Max ÄG	0.1	99.9	6	Kcal/mol
Self Dimer ÄG	0.1	99.9	8	Kcal/mol
Run/Repeat	2	99	5	bases

For 65-75 mers

Parameter	Minimum Value	Maximum Value	Default Value	Unit
Probe Length	10	99	70	bases
Probe Length tolerance	0	15	3
Probe Target Tm	40	99	75	°C
Probe Tm Tolerance (+/- above)	0.1	99	5
Hairpin Max ÄG	0.1	99.9	6	Kcal/mol
Self Dimer ÄG	0.1	99.9	8	Kcal/mol
Run/Repeat	2	99	6	bases

Other Parameters

• Probe Location
  1. 3' end bias: The oligos chosen should be towards the 3' end of the gene i.e. Default : 3' end.
  2. The oligos should be designed by default within 999 bases of 3' end. The range can be from 0 to 1500 bases.

• The oligos should be free of cross homology (i.e They should be BLAST searched against the appropriate genome category).

DNA Microarray with Array Designer: Array Designer is an exceptional software to design highly specific oligos for expression and SNP genotyping microarray experiments.

Applications of Microarrays

Gene discovery: DNA Microarray technology helps in the identification of new genes, know about their functioning and expression levels under different conditions.
Disease diagnosis: DNA Microarray technology helps researchers learn more about different diseases such as heart diseases, mental illness, infectious disease and especially the study of cancer. Until recently, different types of cancer have been classified on the basis of the organs in which the tumors develop. Now, with the evolution of microarray technology, it will be possible for the researchers to further classify the types of cancer on the basis of the patterns of gene activity in the tumor cells. This will tremendously help the pharmaceutical community to develop more effective drugs as the treatment strategies will be targeted directly to the specific type of cancer.
Drug discovery: Microarray technology has extensive application in Pharmacogenomics. Pharmacogenomics is the study of correlations between therapeutic responses to drugs and the genetic profiles of the patients. Comparative analysis of the genes from a diseased and a normal cell will help the identification of the biochemical constitution of the proteins synthesized by the diseased genes. The researchers can use this information to synthesize drugs which combat with these proteins and reduce their effect.
Toxicological research: Microarray technology provides a robust platform for the research of the impact of toxins on the cells and their passing on to the progeny. Toxicogenomics establishes correlation between responses to toxicants and the changes in the genetic profiles of the cells exposed to such toxicants.

Types of Microarrays

Types of Microarrays

Depending upon the kind of immobilized sample used construct arrays and the information fetched, the Microarray experiments can be categorized in three ways:
1. Microarray expression analysis: In this experimental setup, the cDNA derived from the mRNA of known genes is immobilized. The sample has genes from both the normal as well as the diseased tissues. Spots with more more intensity are obtained for diseased tissue gene if the gene is over expressed in the diseased condition. This expression pattern is then compared to the expression pattern of a gene responsible for a disease.
2. Microarray for mutation analysis: For this analysis, the researchers use gDNA. The genes might differ from each other by as less as a single nucleotide base.
A single base difference between two sequences is known as Single Nucleotide Polymorphism (SNP) and detecting them is known as SNP detection.
3. Comparative Genomic Hybridization: It is used for the identification in the increase or decrease of the important chromosomal fragments harboring genes involved in a disease.