Sequence context-specific profiles for homology searching

Biegert, Andreas; Söding, Johannes

doi:10.1073/pnas.0810767106

Cited by 159 publications

(139 citation statements)

References 45 publications

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“…This script uses CS-BLAST, a sequence contextspecific extension of PSI-BLAST, for iterative sequence searching. 45 It also contains heuristics to reduce the inclusion of nonhomologous sequence segments at the ends of PSI-BLAST sequence matches, the leading cause of high-scoring false positive matches in PSI-BLAST. Profile HMMs were calculated from the alignments using hhmake and compared with HHsearch, both from the HHsearch 1.6.0 package.…”

Section: Methodsmentioning

confidence: 99%

A galaxy of folds

et al. 2009

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Many protein classification systems capture homologous relationships by grouping domains into families and superfamilies on the basis of sequence similarity. Superfamilies with similar 3D structures are further grouped into folds. In the absence of discernable sequence similarity, these structural similarities were long thought to have originated independently, by convergent evolution. However, the growth of databases and advances in sequence comparison methods have led to the discovery of many distant evolutionary relationships that transcend the boundaries of superfamilies and folds. To investigate the contributions of convergent versus divergent evolution in the origin of protein folds, we clustered representative domains of known structure by their sequence similarity, treating them as point masses in a virtual 2D space which attract or repel each other depending on their pairwise sequence similarities. As expected, families in the same superfamily form tight clusters. But often, superfamilies of the same fold are linked with each other, suggesting that the entire fold evolved from an ancient prototype. Strikingly, some links connect superfamilies with different folds. They arise from modular peptide fragments of between 20 and 40 residues that co-occur in the connected folds in disparate structural contexts. These may be descendants of an ancestral pool of peptide modules that evolved as cofactors in the RNA world and from which the first folded proteins arose by amplification and recombination. Our galaxy of folds summarizes, in a single image, most known and many yet undescribed homologous relationships between protein superfamilies, providing new insights into the evolution of protein domains.

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Section: Methodsmentioning

confidence: 99%

A galaxy of folds

et al. 2009

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“…In contrast, HHblits calculates pseudocounts that depend on the local sequence context (that is, the 13 positions around each residue). This method had improved the sensitivity and alignment quality of the resulting profile considerably 8 . HHblits then searches the HMM database and adds the sequences from HMMs below a defined expected value (E value) threshold to the query MSA, from which the HMM for the next search iteration is built ( Fig.…”

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confidence: 99%

“…We thus obtain a 219-row extended sequence profile, which can be aligned to extended sequences representing the other profile using fast, standard dynamic programming techniques. We generated the 219-letter alphabet using the same method that was previously used for learning an optimal set of sequence context profiles 8 , but here we set the window size from 13 to 1 residue. We also set the window weights w j to 100 to obtain a hard clustering.…”

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confidence: 99%

HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment

et al. 2011

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are generally too slow for iteratively searching through large sequence databases such as UniProt or NCBI's nonredundant (nr) database. Here we present HMM-HMM-based lightningfast iterative sequence search (HHblits), which extends HHsearch to enable fast, iterative sequence searches. The profile-profile alignment prefilter of HHblits reduces the number of full HMM-HMM alignments from many millions to a few thousand, making it faster than PSI-BLAST but still as sensitive as HHsearch (Supplementary Fig. 1).For iterative searches, HHblits needs a database of HMMs that covers the entire sequence space. We devised a very fast method, kClust (M. Hauser, C.E. Mayer and J.S., unpublished data), for clustering large sequence databases down to 20-30% maximum pairwise sequence identity while requiring almost full-length alignability (>80% coverage of longer sequences). This strict coverage criterion enriches for orthologous sequences with the same domain architecture 7 : of the UniProt20 clusters containing more than two Swiss-Prot sequences with enzyme commission numbers, 98.4% had all four enzyme commission digits conserved ( Supplementary Fig. 2). kClust is sufficiently fast (~1,000 times faster than BLAST) to allow for regular reclustering of the updated UniProt and nr databases. UniProt20 (the version from July 2011) contained 15 million sequences in 2.6 million HMMs, with an average of 5.5 sequences per cluster.HHblits first converts the query sequence (or MSA) to an HMM. This is conventionally done by adding pseudocounts of amino acids that are physicochemically similar to the amino acid in the query. In contrast, HHblits calculates pseudocounts that depend on the local sequence context (that is, the 13 positions around each residue). This method had improved the sensitivity and alignment quality of the resulting profile considerably 8 . HHblits then searches the HMM database and adds the sequences from HMMs below a defined expected value (E value) threshold to the query MSA, from which the HMM for the next search iteration is built ( Fig. 1a and Supplementary Fig. 3). For speed and sensitivity, the prefilter is crucial. The key idea was to implement profile-profile comparison as a sequence-to-profile comparison by discretizing the vectors of 20 amino acid probabilities in each HMM column into an alphabet of 219 letters. Each letter represents a typical profile column ( Supplementary Fig. 4). We approximate the database HMMs by sequences over this extended alphabet, ignoring the insertion and deletion probabilities of the HMMs (Supplementary Fig. 5). Before prefiltering, we calculate the score of each query HMM column with each of the 219 letters, which results in a 219-row extended sequence profile. The prefiltering consists of two steps (Supplementary Fig. 3 Building protein multiple-sequence alignments (MSAs) by iterative sequence searches is of fundamental importance in computational biology, as MSAs are a key intermediate step in the sequence-based prediction of evolutionarily conserved properties, such as tert...

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“…We carried out a profiled search for BH3 domains using structural HsBcl-xL and sequence motifs from known BH3-only proteins as a template [18]. This yielded four Hydra gene models encoding novel proteins with conserved BH3 domains.…”

Section: Bh3-only Proteinsmentioning

confidence: 99%

The molecular cell death machinery in the simple cnidarian Hydra includes an expanded caspase family and pro- and anti-apoptotic Bcl-2 proteins

et al. 2010

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The fresh water polyp Hydra belongs to the phylum Cnidaria, which diverged from the metazoan lineage before the appearance of bilaterians. In order to understand the evolution of apoptosis in metazoans, we have begun to elucidate the molecular cell death machinery in this model organism. Based on ESTs and the whole Hydra genome assembly, we have identified 15 caspases. We show that one is activated during apoptosis, four have characteristics of initiator caspases with N-terminal DED, CARD or DD domain and two undergo autoprocessing in vitro. In addition, we describe seven Bcl-2-like and two Bak-like proteins. For most of the Bcl-2 family proteins, we have observed mitochondrial localization. When expressed in mammalian cells, HyBak-like 1 and 2 strongly induced apoptosis. Six of the Bcl-2 family members inhibited apoptosis induced by camptothecin in mammalian cells with HyBcl-2-like 4 showing an especially strong protective effect. This protein also interacted with HyBak-like 1 in a yeast two-hybrid assay. Mutation of the conserved leucine in its BH3 domain abolished both the interaction with HyBak-like 1 and the anti-apoptotic effect. Moreover, we describe novel Hydra BH-3-only proteins. One of these interacted with Bcl-2-like 4 and induced apoptosis in mammalian cells. Our data indicate that the evolution of a complex network for cell death regulation arose at the earliest and simplest level of multicellular organization, where it exhibited a substantially higher level of complexity than in the protostome model organisms Caenorhabditis and Drosophila.

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Sequence context-specific profiles for homology searching

Cited by 159 publications

References 45 publications

A galaxy of folds

A galaxy of folds

HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment

The molecular cell death machinery in the simple cnidarian Hydra includes an expanded caspase family and pro- and anti-apoptotic Bcl-2 proteins

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