MOTIVATION:Protein sequence searching and alignment are fundamental tools of modern biology. Alignments are assessed using their similarity scores, essentially the sum of substitution matrix scores over all pairs of aligned amino acids. We previously proposed a generative probabilistic method that yields scores that take the sequence context around each aligned residue into account. This method showed drastically improved sensitivity and alignment quality compared with standard substitution matrix-based alignment.RESULTS:Here, we develop an alternative discriminative approach to predict sequence context-specific substitution scores. We applied our approach to compute context-specific sequence profiles for Basic Local Alignment Search Tool (BLAST) and compared the new tool (CS-BLASTdis) to BLAST and the previous context-specific version (CS-BLASTgen). On a dataset filtered to 20% maximum sequence identity, CS-BLASTdisis was 51% more sensitive than BLAST and 17% more sensitive than CS-BLASTgenin, detecting remote homologues at 10% false discovery rate. At 30% maximum sequence identity, its alignments contain 21 and 12% more correct residue pairs than those of BLAST and CS-BLASTgen, respectively. Clear improvements are also seen when the approach is combined with PSI-BLAST and HHblits. We believe the context-specific approach should replace substitution matrices wherever sensitivity and alignment quality are critical.

Sequence-based protein function and structure prediction depends crucially on sequence-search sensitivity and accuracy of the resulting sequence alignments. We present an open-source, general-purpose tool that represents both query and database sequences by profile hidden Markov models (HMMs): 'HMM-HMM-based lightning-fast iterative sequence search' (HHblits; http://toolkit.genzentrum.lmu.de/hhblits/). Compared to the sequence-search tool PSI-BLAST, HHblits is faster owing to its discretized-profile prefilter, has 50-100% higher sensitivity and generates more accurate alignments.

Outer membrane beta-barrels (OMBBs) are the major class of outer membrane proteins from Gram-negative bacteria, mitochondria, and plastids. Their transmembrane domains consist of 8-24 beta-strands forming a closed, barrel-shaped beta-sheet around a central pore. Despite their obvious structural regularity, evidence for an origin by duplication or for a common ancestry has not been found. We use three complementary approaches to show that all OMBBs from Gram-negative bacteria evolved from a single, ancestral beta beta hairpin. First, we link almost all families of known single-chain bacterial OMBBs with each other through transitive profile searches. Second, we identify a clear repeat signature in the sequences of many OMBBs in which the repeating sequence unit coincides with the structural beta beta hairpin repeat. Third, we show that the observed sequence similarity between OMBB hairpins cannot be explained by structural or membrane constraints on their sequences. The third approach addresses a longstanding problem in protein evolution: how to distinguish between a very remotely homologous relationship and the opposing scenario of "sequence convergence." The origin of a diverse group of proteins from a single hairpin module supports the hypothesis that, around the time of transition from the RNA to the protein world, proteins arose by amplification and recombination of short peptide modules that had previously evolved as cofactors of RNAs.

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.

Sequence alignment and database searching are essential tools in biology because a protein's function can often be inferred from homologous proteins. Standard sequence comparison methods use substitution matrices to find the alignment with the best sum of similarity scores between aligned residues. These similarity scores do not take the local sequence context into account. Here, we present an approach that derives context-specific amino acid similarities from short windows centered on each query sequence residue. Our results demonstrate that the sequence context contains much more information about the expected mutations than just the residue itself. By employing our context-specific similarities (CS-BLAST) in combination with NCBI BLAST, we increase the sensitivity more than 2-fold on a difficult benchmark set, without loss of speed. Alignment quality is likewise improved significantly. Furthermore, we demonstrate considerable improvements when applying this paradigm to sequence profiles: Two iterations of CSI-BLAST, our context-specific version of PSI-BLAST, are more sensitive than 5 iterations of PSI-BLAST. The paradigm for biological sequence comparison presented here is very general. It can replace substitution matrices in sequence- and profile-based alignment and search methods for both protein and nucleotide sequences.

Generation of context profile library N = 1 million training profiles of length l = 2d+1 were generated as described in the main text and Figure 2. Each training profile is represented by a count profile cn(j, x), which specifies the counts of amino acid x ∈ {1, . . . , 20} at position j ∈ {−d, . . . , d}. These counts are obtained by multiplying the sequence profile tn(j, x) by the effective number of sequences Nn(j) at position j in the alignment from which training profile tn(j, x) was calculated: cn(j, x) = Nn(j)tn(j, x) (see next section for details). Here, we describe how these N profiles are clustered in order to obtain a set of K context profiles which recur frequently among the training profiles and which together can describe all training profiles. More precisely, we seek to determine context profiles p = (p1, . . . , pK) and their prior probabilities α = (α1, . . . , αK) that maximize the likelihoodP (c|p, α) that the training profile counts c = (c1, . . . , cN ) were generated by the context profiles. We model the distribution of counts cn(j, x) in each column j by a multinomial distribution. Since cn(j, x) can be real-valued, however, we replace the factorials in the multinomial distribution by Gamma functions (n! = Γ(n + 1)). The probability for context profile pk to have emitted counts cn(j, x) (j ∈ {−d, . . . , d}, x ∈ {1, . . . , 20}) is

With over 47,000 citations, the sequence search method BLAST has been an essential tool in biological research since its development in 1990. By accounting for the influence of sequence context on the mutation probabilities of amino acids, context-specific BLAST (CS-BLAST) achieves two-fold higher sensitivity for distantly related protein sequences, at the same speed and error rate.

Automated protein structure prediction is becoming a mainstream tool for biological research. This has been fueled by steady improvements of publicly available automated servers over the last decade, in particular their ability to build good homology models for an increasing number of targets by reliably detecting and aligning more and more remotely homologous templates. Here, we describe the three fully automated versions of the HHpred server that participated in the community-wide blind protein structure prediction competition CASP8. What makes HHpred unique is the combination of usability, short response times (typically under 15 min) and a model accuracy that is competitive with those of the best servers in CASP8.

Background: During the last years, methods for remote homology detection have grown more and more sensitive and reliable. Automatic structure prediction servers relying on these methods can generate useful 3D models even below 20% sequence identity between the protein of interest and the known structure ( template). When no homologs can be found in the protein structure database (PDB), the user would need to rerun the same search at regular intervals in order to make timely use of a template once it becomes available.Results: PDBalert is a web-based automatic system that sends an email alert as soon as a structure with homology to a protein in the user's watch list is released to the PDB database or appears among the sequences on hold. The mail contains links to the search results and to an automatically generated 3D homology model. The sequence search is performed with the same software as used by the very sensitive and reliable remote homology detection server HHpred, which is based on pairwise comparison of Hidden Markov models.Conclusion: PDBalert will accelerate the information flow from the PDB database to all those who can profit from the newly released protein structures for predicting the 3D structure or function of their proteins of interest.

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

Motivation: Phospholipid scramblases ( PLSCRs) constitute a family of cytoplasmic membrane-associated proteins that were identified based upon their capacity to mediate a Ca(2+)-dependent bidirectional movement of phospholipids across membrane bilayers, thereby collapsing the normally asymmetric distribution of such lipids in cell membranes. The exact function and mechanism( s) of these proteins nevertheless remains obscure: data from several laboratories now suggest that in addition to their putative role in mediating transbilayer flip/flop of membrane lipids, the PLSCRs may also function to regulate diverse processes including signaling, apoptosis, cell proliferation and transcription. A major impediment to deducing the molecular details underlying the seemingly disparate biology of these proteins is the current absence of any representative molecular structures to provide guidance to the experimental investigation of their function.Results: Here, we show that the enigmatic PLSCR family of proteins is directly related to another family of cellular proteins with a known structure. The Arabidopsis protein At5g01750 from the DUF567 family was solved by X-ray crystallography and provides the first structural model for this family. This model identifies that the presumed C-terminal transmembrane helix is buried within the core of the PLSCR structure, suggesting that palmitoylation may represent the principal membrane anchorage for these proteins. The fold of the PLSCR family is also shared by Tubby-like proteins. A search of the PDB with the HHpred server suggests a common evolutionary ancestry. Common functional features also suggest that tubby and PLSCR share a functional origin as membrane tethered transcription factors with capacity to modulate phosphoinositide-based signaling.

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 repea- ted 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 identifica- tion 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 mem- brane -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 (S¨ oding et al., 2006), the new method (1) generates a multiple alignment of repeats; (2) utilizes the transitive nature of homology through a novel merging pro- cedure with fully probabilistic treatment of alignments; (3) improves alignment quality through an algorithm that maximizes the expec- ted accuracy; (4) is able to identify different kinds of repeats within complex architectures by a probabilistic domain boundary detection method, (5) improves sensitivity through a new approach to assess statistical significance.

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 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

How did today's complex protein domains originate? We believe that protein domains arose as combinations of peptide modules that originally evolved as cofactors in the RNA world (SL03). Here, we investigate the hypothesis that the hairpin that forms the repeating structural unit of the transmembrane -barrels (TMBBs) represents such an ancestral peptide. Despite the obvious structural similarity between TMBBs and the resulting amphipathic character of their sequences, sequence similarity is hardly detectable (NLL95) and evidence for a common ancestry of all bacterial TMBBs has not been found. We use three approaches to test the hypothesis that TMBBs originated by multiple duplication of an ancestral hairpin. First, we investigate the evolutionary relationship between the various groups of TMBB proteins. Second, we look for the hairpin repeats in the sequences of TMBBs with a novel, highly sensitive de-novo repeat detection method. Finally, we combine analysis of structural and sequence similarity to show that the observed sequence similarity between TMBBs cannot be explained by structural constraints on the sequence and hence is a sign of their common origin. In the rst approach, we use a sensitive remote homology detection method (HHsenser) that performs exhaustive transitive prole search using HMM-HMM comparison (SRBL06). Starting from single TMBBs we are able to nd most known groups of bacterial TMBBs. To get maximum coverage, we pool the largely overlapping results from 17 HHsenser searches started from representative TMBBs with known structure. A clustermap of these proteins was generated by CLANS (FL04) (Fig 1) and all clusters in the map were analyzed and annotated. Strikingly, all known groups of bacterial TMBBs can be found except the non-canonical TMBBs, which form a closed barrel from oligomers: the TolC-like proteins (3 4 strands), the -hemolysins (7 2 strands) and MspA (8 2 strands). Among the 486 clusters we found about 100 clusters of hypothetical proteins and only four false positives. In the second approach, we apply our new de-novo repeat detection method HHrepID to all TMBBs with known structure. In about half of the TMBBs, we are able to identify a clean repeat pattern whose repeat unit coincides with the hairpins. This is the rst time such a repeat pattern has been observed in TMBBs. In the third approach, we divide a representative set of canonical TMBBs into double hairpin units and search both the set of TMBBs and the rest of the PDB for similar structural fragments. For each structural match a sequence similarity (prole-prole) score is calculated using the x ed structural aligment. In a scatter plot of structural versus sequence similarity, one nds that matches between double hairpins from different TMBB folds (the putative homologs) have much higher sequence similarities on average than matches between TMBBs and non-TMBBs (analogs) at the same structural similarities. We can thus exclude simple structural constraints as the cause of the observed sequence similarity between TMBBs. We nally show that the matches of TMBBs with noncanonical -hemolysin and MspA proteins lie within the analog distribution, proving that even the constraints associated with being embedded in a membrane environment can not explain the sequence similarity between TMBBs. All three computational approaches thus support the common origin of canonical bacterial TMBBs by sequential duplication of an ancestral hairpin.

HHsenser is the first server to offer exhaustive intermediate profile searches, which it combines with pairwise comparison of hidden Markov models. Starting from a single protein sequence or a multiple alignment, it can iteratively explore whole superfamilies, producing few or no false positives. The output is a multiple alignment of all detected homologs. HHsenser's sensitivity should make it a useful tool for evolutionary studies. It may also aid applications that rely on diverse multiple sequence alignments as input, such as homology-based structure and function prediction, or the determination of functional residues by conservation scoring and functional subtyping.HHsenser can be accessed at http://hhsenser.tuebingen.mpg.de/. It has also been integrated into our structure and function prediction server HHpred (http://hhpred.tuebingen.mpg.de/) to improve predictions for near-singleton sequences.

HHrep is a web server for the de novo identification of repeats in protein sequences, which is based on the pairwise comparison of profile hidden Markov models (HMMs). Its main strength is its sensitivity, allowing it to detect highly divergent repeat units in protein sequences whose repeats could as yet only be detected from their structures. Examples include sequences with beta-propellor fold, ferredoxin-like fold, double psi barrels or (beta a)(8) (TIM) barrels. We illustrate this with proteins from four superfamilies of TIM barrels by revealing a clear 4- and 8- fold symmetry, which we detect solely from their sequences. This symmetry might be the trace of an ancient origin through duplication of a beta a beta a or beta a unit. HHrep can be accessed at http://hhrep. tuebingen.mpg.de.

The MPI Bioinformatics Toolkit is an interactive web service which offers access to a great variety of public and in-house bioinformatics tools. They are grouped into different sections that support sequence searches, multiple alignment, secondary and tertiary structure prediction and classification. Several public tools are offered in customized versions that extend their functionality. For example, PSI-BLAST can be run against regularly updated standard databases, customized user databases or selectable sets of genomes. Another tool, Quick2D, integrates the results of various secondary structure, transmembrane and disorder prediction programs into one view. The Toolkit provides a friendly and intuitive user interface with an online help facility. As a key feature, various tools are interconnected so that the results of one tool can be forwarded to other tools. One could run PSI-BLAST, parse out a multiple alignment of selected hits and send the results to a cluster analysis tool. The Toolkit framework and the tools developed in-house will be packaged and freely available under the GNU Lesser General Public Licence (LGPL). The Toolkit can be accessed at http://toolkit.tuebingen.mpg.de.

HHpred is a fast server for remote protein homology detection and structure prediction and is the first to implement pairwise comparison of profile hidden Markov models (HMMs). It allows to search a wide choice of databases, such as the PDB, SCOP, Pfam, SMART, COGs and CDD. It accepts a single query sequence or a multiple alignment as input. Within only a few minutes it returns the search results in a user-friendly format similar to that of PSI-BLAST. Search options include local or global alignment and scoring secondary structure similarity. HHpred can produce pairwise query-template alignments, multiple alignments of the query with a set of templates selected from the search results, as well as 3D structural models that are calculated by the MODELLER software from these alignments. A detailed help facility is available. As a demonstration, we analyze the sequence of SpoVT, a transcriptional regulator from Bacillus subtilis. HHpred can be accessed at http://protevo.eb.tuebingen.mpg.de/hhpred.