An alternative view of protein fold spaceстатья из журнала
Аннотация: Comparing and subsequently classifying protein structures information has received significant attention concurrent with the increase in the number of experimentally derived 3-dimensional structures. Classification schemes have focused on biological function found within protein domains and on structure classification based on topology. Here an alternative view is presented that groups substructures. Substructures are long (50–150 residue) highly repetitive near-contiguous pieces of polypeptide chain that occur frequently in a set of proteins from the PDB defined as structurally non-redundant over the complete polypeptide chain. The substructure classification is based on a previously reported Combinatorial Extension (CE) algorithm that provides a significantly different set of structure alignments than those previously described, having, for example, only a 40% overlap with FSSP. Qualitatively the algorithm provides longer contiguous aligned segments at the price of a slightly higher root-mean-square deviation (rmsd). Clustering these alignments gives a discreet and highly repetitive set of substructures not detectable by sequence similarity alone. In some cases different substructures represent all or different parts of well known folds indicative of the Russian doll effect—the continuity of protein fold space. In other cases they fall into different structure and functional classifications. It is too early to determine whether these newly classified substructures represent new insights into the evolution of a structural framework important to many proteins. What is apparent from on-going work is that these substructures have the potential to be useful probes in finding remote sequence homology and in structure prediction studies. The characteristics of the complete all-by-all comparison of the polypeptide chains present in the PDB and details of the filtering procedure by pair-wise structure alignment that led to the emergent substructure gallery are discussed. Substructure classification, alignments, and tools to analyze them are available at http://cl.sdsc.edu/ce.html. Proteins 2000;38:247–260. © 2000 Wiley-Liss, Inc.
Год издания: 2000
Авторы: Ilya N. Shindyalov, Philip E. Bourne
Издательство: Wiley
Источник: Proteins Structure Function and Bioinformatics
Ключевые слова: Protein Structure and Dynamics, Enzyme Structure and Function, Machine Learning in Bioinformatics
Другие ссылки: Proteins Structure Function and Bioinformatics (HTML)
PubMed (HTML)
PubMed (HTML)
Открытый доступ: closed
Том: 38
Выпуск: 3
Страницы: 247–260