De novo identification of highly diverged protein repeats by probabilistic consistency

By Andreas Biegert, Johannes Söding. 2008

153 citations recorded by the source corpus.

MOTIVATION An estimated 25% of all eukaryotic proteins contain repeats, which underlines the importance of duplication for evolving new protein functions. Internal repeats often correspond to structural or functional units in proteins. Methods capable of identifying diverged repeated segments or domains at the sequence level can therefore assist in predicting domain structures, inferring hypotheses about function and mechanism, and investigating the evolution of proteins from smaller fragments. RESULTS We present HHrepID, a method for the de novo identification of repeats in protein sequences. It is able to detect the sequence signature of structural repeats in many proteins that have not yet been known to possess internal sequence symmetry, such as outer membrane beta-barrels. HHrepID uses HMM-HMM comparison to exploit evolutionary information in the form of multiple sequence alignments of homologs. In contrast to a previous method, the new method (1) generates a multiple alignment of repeats; (2) utilizes the transitive nature of homology through a novel merging procedure with fully probabilistic treatment of alignments; (3) improves alignment quality through an algorithm that maximizes the expected accuracy; (4) is able to identify different kinds of repeats within complex architectures by a probabilistic domain boundary detection method and (5) improves sensitivity through a new approach to assess statistical significance. AVAILABILITY Server: http://toolkit.tuebingen.mpg.de/hhrepid; Executables: ftp://ftp.tuebingen.mpg.de/pub/protevo/HHrepID

Subfields: Cellular and Molecular Neuroscience, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cellular and Molecular Neuroscience

In Molecular Biology

Citations in the corpus

The MPI bioinformatics Toolkit as an integrative platform for advanced protein sequence and structure analysis (2016, methodology): “An overview of tools available in the Toolkit Category Tools Search CS-BLAST (6), HHblits (7), HHpred (5), HHsenser (45), HMMER3 (46), PatternSearch, ProtBLAST (9), ProtBLAST+ (15), PSI-BLAST (14), PSI-BLAST+ (15), SimShiftDB (47), PDBalert (37) Alignment AlignmentViewer, Blammer, Clustal Omega (38)”
Protein sequence comparison and fold recognition: progress and good-practice benchmarking. (2011, methodology): “Finally, the iterative search methods HHMER3 and HHblits (see below) replace the Smith–Waterman algorithm by the maximum accuracy algorithm [43,44], which is much less prone to overextension.”
A conditional neural fields model for protein threading (2012, methodology): “For MAP the Maximum Expected Accuracy (MEA) algorithm (Biegert and Söding, 2008) is used to generate alignments.”
HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment (2011, methodology): “We extended this algorithm to the case of local HMM-HMM comparison[10], which pro-”
Fast and accurate automatic structure prediction with HHpred (2009, methodology): “HHsearch employs the Viterbi algorithm for ranking the database matches but realigns the best matches with a local HMM-HMM alignment version of the more accurate Maximum Accuracy (MAC) algorithm,11,12 which maximizes the expected number of correctly aligned residues.”
The Transporter Classification Database (TCDB): recent advances (2015, methodology): “TCDB Topology Stats (IHb) Statistically analyzes integral membrane proteins for topology in families or superfamilies HHrepID (67) Detects protein repeats using a dot-plot for analysis”