Inferring function from structure

Structure and function can be transferred between similar sequences because they have been conserved over long periods of time. Above 40% sequence identity, homologous proteins tend to have the same function.

Function: Biochemical: the chemical interactions ocurring in a protein; Biological: the role within the cell of the protein; Phenotypic: the role played by the protein in the organism as a whole.

EC (Enzyme Commission) provides a widely used protein functiona classification scheme. There are several databases containing funcional information: SWISS-PROT, GenProtEC, etc. There exist also multifunctional proteins. Gene Ontologies uses a controlled vocabulary for describing the roles of genes and gene products in any organism: (biological, molecular, cellular).

Functional information which can be obtained from 3D protein structures

• Basic structure: in the form of a PDB file.
• Protein-ligand complexes: can provide the biochemical function of the protein.

Relationship between structure and function

Protein structural classification is not of much help since some structures are under-represented. Furthermore, as the number of folds in limited in nature, similar structures can have totally different functions. Most folds have a homologous familiy associated with them, and it is expected that family members will have related function. There are, however, examples of divergence of function.

Analogues: some functions have different structural solutions (examples of convergent evolution).

Assigning function from structure

Ab initio prediction: a protein-ligand binding site (active site) is often found to be the largest cleft in the protein.

Structural comparisons: using structural databases such as CATH or SCOP. It is the most powerful method. Sometimes structural similarit can be the result of convergent evolution.

Structural motifs: detailed knowledge of the active site is required. Six methods:

SITE and SITE-Match: correlates an alignment with PDB and SWISS-PROT files.
TESS: 3D Template Search and Superposition.
Fuzzy Functional Forms (FFFs): derives FFFs from 3D structural information.
SPASM, RIGOR: tools for studying constellations of small number of residues.
Molecular Recognition: searches for similar spatial arrangements of atoms around a particular chemical moiety in proteins by superposing them.
Protein Side Chain Patterns: detects active site in proteins via recurring amino acid side-chain patterns.

Modern Drug Discovery

Target identification:

• usually through biological or genetic investigation.
• An assay to look for modulators (either inhibitors, antagonists, or agonists) of the target activity.
• High-throughput screen (HTS).
• Elaboration of the initial small molecule hit through medicinal chemistry: combinatorial chemistry, quantitative structure activity relationships (QSAR), computer-aided drug design (CADD) and structure-based drug design.
• Lead optimization into a candidate drug: multidimensional optimization problem searching within the relatively limited chemical space of analogs of the lead compound.
• Large-scale production methods, preclinical animal safety studies, clinical trials.

Structural Bioinformatics on Drug DiscoveryTarget Assessment:

Target druggability: the energetically optimal protein would be spherical, with all its hydrophobic residues pointing inward.

A quantitative approach is the rule of five (Lipinski):A compound is likely to show poor absorption or permeation if:

• It has more than five hydrogen bond donors
• The molecular weight is over 500
• The Clog P (calculated octanol/water partition coefficient) is over five
• The sum of nitrogens and oxygens is over 10
• Weak inhibition (<100nm)>

Another physicochemical complementary properties: surface area and volume of the pocket, hydrophobicity and hydrophilic character, curvature and shape of the pocket.

Target Triage: computer-aided target selection (CATS), based on the importance of a gene for the organism, the occurrence of the gene in multiple target species, specificity or inhibition by reference to sequence similarity, and easiness of assay. The group of residues lining a ligand-binding site are of moreimportance than long-range interaction and conformational changes.

Target Validation: knock-out of the gene of interest or RNA antisense technology to inactivate the gene.Lead Identification: structural bioinformatics can be used for function and ligand prediction. Using structural similarity to find chemical leads (usually, when the proteins share less than 30% sequence identity the active sites are nonidentical).

Virtual screening: to derive a pharmacophore describing the functionally important sites in a ligand-binding site, and docking and scoring. Creating a chemical library.

Lead Optimization: repeated cycles of determining the structure of the target in complex with a number of lead compounds and their analogs. Structural bioinformatics should be used to design suitable constructs of the outset of the project. Alignment and secondary structure prediction on multiple alignments. Homology modeling and the use of a surrogate protein (an orthologous protein from another species or a similar member of the same gene family).

ADMET Modeling: additional parameters that can affect the biopharmaceutical and safety properties of the drug: in vivo absorption, distribution, metabolism, excretion, and toxicology. Sequence-structure relationship and protein homology modeling can be used in ADMET modeling.

Bioinformatics Market Is On Explosion

Author: James Marriot

In the current scenario, bioinformatics market size stands at approximately $840 million. It's poised for a rapid growth with increase of around $1.82 billion by 2007. The Compound Annual Growth Rate - CAGR of the Bioinformatics Market is likely to be around 15 % up till 2010.In the last few years, bioinformatics has achieved acceptance among other markets particularly biotechnology, pharmaceutical, agricultural biotechnology and industrial biotechnology.

The bioinformatics industry is among one of the largest sector within super-computing and stands to benefit appreciably from acceleration of FPGA-based applications. Bioinformatics involves annotation, recording, analysis, storage, and searching/extracting of molecular information such as protein sequences, gene sequences, cell activity and variation in genetic structures.

Bioinformatics is mainly used in area of life sciences like molecular medicine, drug discovery, microbial genome applications, comparative studies, agriculture and others. The contribution of Bioinformatics to these areas of life sciences is evident by its application in the research fields e.g. proteomics, genomics.

In the near future the contribution of genomics in the field of drug discovery and development is predicted to be one of the highest. Over the long haul, increasing number of new drugs designed would be genomic-related. However, after human genome mapping the use of proteomics is likely to rise at a higher rate than that of genomics, as it is proteins, not genes, which the researchers would use in the coming years for developing new drugs.

The growth of the Bioinformatics market is stable as 20% of the use is based upon proteomics and genomics. Many Research and development centers across the world are looking for a base to transfer the data, information and knowledge to facilitate an informatics-based decision support system.

However, biotechnology and pharmaceutical companies will continue to look at the tools for understanding the association between biological data types. As bioinformatics is broadly more useful to discovery and development, the end users will adopt solutions that streamline tracking, access, sharing of data and interpretation of different data types with least impact on resources. In consequence, hundreds of suppliers are embarking upon the bioinformatics bandwagon.

For further information about bioinformatics market please read the report "Bioinformatics Market Update (2006)" published by RNCOS at http://www.rncos.com/Report/COM31.htm