Abhishek Tiwari:STRUCTURAL BIOINFORMATICS
In Silico Biology Volume 7 | Article 7 | 2006
- Evolved cellular automata for protein secondary structure prediction imitate the determinants for folding observed in nature
Authors have developed a simple algorithm based on cellular automata for secondary structure predictions of proteins.Cellular Automata have been used in Bioinformatics for different purposes like predicting protein subcellular location and modeling biochemical pathways.Cellular automata make use of local interactions to simulate global and decentralized phenomena which can help to study protein folding phenomenon.Protein Folding depends on both local interactions between adjacent residues and global interactions between distant residues. A cellular automaton can capture both of these interactions making it an ideal candidate for studying protein folding. Developed program takes protein sequence as input and output is the corresponding sequence of secondary conformations adopted by each residue.
PLoS Computational Biology Volume 2 | Issue 10 | OCTOBER 2006
- 3D Complex: a Structural Classification of Protein Complexes
Most of the proteins in a cell assemble into complexes to carry out their function. It is therefore crucial to understand the physico-chemical properties as well as the evolution of interactions between proteins. The Protein Data Bank represents an important source of information for such studies, because more than half of the structures are homo- or heteromeric protein complexes. Here Emmanuel D Levy,etl. propose the first hierarchical classification of whole protein complexes of known three-dimensional structure, based on representing their fundamental structural features as a graph. This classification provides the first overview of all the complexes in the Protein Data Bank and allows non-redundant sets to be derived at different levels of detail. This reveals that between one half and two thirds of known structures are multimeric, depending on the level of redundancy accepted. We also analyse the structures in terms of the topological arrangement of their subunits, and find that they form a small number of arrangements compared to all theoretically possible ones. This is because most complexes contain four subunits or less, and the large majority are homomeric. In addition, there is a strong tendency for symmetry in complexes, even for heteromeric complexes. Finally, through comparison of Biological Units in the Protein Data Bank with the Protein Quaternary Structure database, they identified many possible errors in quaternary structure assignments. Our classification, available as a database and web server at http://www.3Dcomplex.org, will be a starting point for future work aimed at understanding the structure and evolution of protein complexes.
Oxford Bioinformatics Volume 22 | Number 22 | 15 November 2006
- Protein–RNA interactions: exploring binding patterns with a three-dimensional superposition analysis of high resolution structures
The recognition of specific RNA sequences and structures by proteins is critical to our understanding of RNA processing, gene expression and viral replication. The diversity of RNA structures suggests that RNA recognition is substantially different than that of DNA.The atomic coordinates of 41 protein–RNA complexes have been used to probe composite nucleoside binding pockets that form the structural and chemical underpinnings of base recognition. Composite nucleoside binding pockets were constructed using three-dimensional superpositions of each RNA nucleoside. Unlike protein–DNA interactions which are dominated by accessibility, RNA recognition frequently occurs in non-canonical and single-strand-like structures that allow interactions to occur from a much wider set of geometries and make fuller use of unique base shapes and hydrogen-bonding ability. By constructing composites that include all van der Waals, hydrogen-bonding, stacking and general non-polar interactions made to a particular nucleoside, the strategies employed are made readily visible. Protein–RNA interactions can result in the formation of a glove-like tight binding pocket around RNA bases, but the size, shape and non-polar binding patterns differ between specific RNA bases. We show that adenine can be distinguished from guanine based on the size and shape of the binding pocket and steric exclusion of the guanine N2 exocyclic amino group. The unique shape and hydrogen-bonding pattern for each RNA base allow proteins to make specific interactions through a very small number of contacts, as few as two in some cases.