ELM: the status of the 2010 eukaryotic linear motif resource

Linear motifs are short segments of multidomain proteins that provide regulatory functions independently of protein tertiary structure. Much of intracellular signalling passes through protein modifications at linear motifs. Many thousands of linear motif instances, most notably phosphorylation sites, have now been reported. Although clearly very abundant, linear motifs are difficult to predict de novo in protein sequences due to the difficulty of obtaining robust statistical assessments. The ELM resource at http://elm.eu.org/ provides an expanding knowledge base, currently covering 146 known motifs, with annotation that includes >1300 experimentally reported instances. ELM is also an exploratory tool for suggesting new candidates of known linear motifs in proteins of interest. Information about protein domains, protein structure and native disorder, cellular and taxonomic contexts is used to reduce or deprecate false positive matches. Results are graphically displayed in a ‘Bar Code’ format, which also displays known instances from homologous proteins through a novel ‘Instance Mapper’ protocol based on PHI-BLAST. ELM server output provides links to the ELM annotation as well as to a number of remote resources. Using the links, researchers can explore the motifs, proteins, complex structures and associated literature to evaluate whether candidate motifs might be worth experimental investigation

Application and implementation of probabilistic profile-profile comparison methods for protein fold recognition

Wydział ChemiiMetody rozpoznawania pofałdowania białka zwane też rozpoznawaniem foldów (eng. Fold Recognition) są metodami wykrywania i przewidywania struktury trzeciorzędowej białka, stosowanymi dla białek, które nie posiadają sekwencji homologicznych o znanej strukturze trzeciorzędowej, zdeponowanych w międzynarodowej bazie danych struktur białkowych (eng. Protein Data Bank). Metody te opierają się na założeniu, że w wyniku ewolucji oraz ogranczeń fizycznych i chemicznych w przyrodzie znajduje się określona i ograniczona liczba odmiennych zwojów białek. Uliniowienia w profilach sekwencyjnych metod profil-profil mogą być obliczane przy pomocy iloczynu skalarnego, modelu probablistycznego, stochastycznego albo przy pomocy miar teoretycznych. Zaprezentowane tu zastosowania i wdrożenia metod porównywania białek typu profil-profil wskazują na zalety zastosowania probablistycznych funkcji oceniających jakość porównania profili nad innymi metodami rozpoznawania foldów. Celem pracy jest wskazanie iż metody porównywania profil-profil mogą przewyższać inne metody rozpoznawania foldów w analizie spokrewnionych białek, i że mogą być one stosowane nie tylko do rozpoznawania foldów, ale także do innych celów takich jak wykrywanie i identyfikacja genów, granic domen białkowych oraz modelowania złożonych struktur białkowych.Fold recognition is a method of fold detecting and protein tertiary structure prediction applied for proteins lacking homologues sequences of known fold and structure deposited in the Protein Data Bank. They are based on assumption that there is strictly limited number of different protein folds in nature, mostly as a result of evolution and due to basic physical and chemical constraints of polypeptide chains. Every newly discovered protein sequence has significant chances to be classified into one of those folds. Many different approaches have been proposed for finding the correct fold for a new sequence and it is often useful to include evolutionary information for query as well as for target proteins. These fold recognition techniques are called profile-profile methods. Here are presented applications and implementations of probabilistic profile-profile comparison methods and advantages of usage of probabilistic scoring function over comparable fold recognition techniques. The purpose of this comparison is to show that probabilistic profile-profile methods may outperform other fold recognition methods in comparison in analysis of distantly related proteins and that they can be applied not only for fold recognition but also for slightly different purposes like gene identification, detection of domain boundaries and modeling of complex protein