Bioinformatics Research Group @IPM

Computational Haplotype Analysis

One of the major interests of current genomics research is disease-gene association, that is, identifying which DNA variation or a set of DNA variations is highly associated with a specific disease. In particular, single nucleotide polymorphisms (SNPs), which are the most common form of DNA variation on the human genome, and a set of SNPs on one chromosome, referred to as a haplotype, are at the forefront of the disease-gene association studies. In general, when haplotype information is used for studying disease-gene association, it is called haplotype analysis. Numerous studies have shown that haplotype analysis can successfully identify the DNA variations relevant to several common and complex human diseases. However, despite its advantages over other approaches, the use of haplotype analysis has been limited due to the high cost and long operation time of bio-molecular methods for obtaining the haplotype information. To address this limitation, two computational procedures, namely, Haplotype Phasing and Tag SNP Selection have been incorporated in haplotype analysis, and now provide the most practical framework for conducting large-scale association studies.

Protein Structure Prediction

Protein structure prediction is one of the most significant technologies pursued by computational structural biology and theoretical chemistry. It has the aim of determining the two- (or three-) dimensional structure of proteins from their amino acid sequences. In more formal terms, this is expressed as the prediction of protein secondary (or tertiary) structure from primary structure. Given the usefulness of known protein structures in such valuable tasks as rational drug design this is a highly active field of research.

Protein-Protein Interaction Prediction

Protein-protein interaction prediction is a field combining bioinformatics and structural biology in an attempt to identify and catalog interactions between pairs or groups of proteins. Understanding protein-protein interactions is important in investigating intracellular signaling pathways. Experimentally, interactions between pairs of proteins are inferred from yeast two-hybrid systems, from affinity purification/mass spectrometry assays, or from protein microarrays. In parallel to the experimental determination of the interactome, computational methods are being developed.

The field of protein-protein interaction prediction is closely related to the field of protein-protein docking, which attempts to use geometric and steric considerations to fit two proteins of known structure into a bound complex. This is a useful mode of inquiry in cases where both proteins in the pair have known structures and are known (or at least strongly suspected) to interact, but since so many proteins do not have experimentally determined structures, sequence-based interaction prediction methods are especially useful in conjunction with experimental studies of an organism's interactome.

Protein Structure Alignment

Structural alignment is a form of sequence alignment based on comparison of shape. These alignments attempt to establish equivalences between two or more polymer structures based on their shape and three-dimensional conformation. This process is usually applied to protein tertiary structures but can also be used for large RNA molecules. In contrast to simple structural superposition, where at least some equivalent residues of the two structures are known, structural alignment requires no a priori knowledge of equivalent positions. Structural alignment is a valuable tool for the comparison of proteins with low sequence similarity, where evolutionary relationships between proteins cannot be easily detected by standard sequence alignment techniques. Structural alignment can therefore be used to imply evolutionary relationships between proteins that share very little common sequence. However, caution should be used in using the results as evidence for shared evolutionary ancestry because of the possible confounding effects of convergent evolution by which multiple unrelated amino acid sequences converge on a common tertiary structure.

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