PROCAIN server for remote protein sequence similarity search

Sensitive and accurate detection of distant protein homology is essential for the studies of protein structure, function and evolution. We recently developed PROCAIN, a method that is based on sequence profile comparison and involves the analysis of four signals—similarities of residue content at the profile positions combined with three types of assisting information: sequence motifs, residue conservation and predicted secondary structure. Here we present the PROCAIN web server that allows the user to submit a query sequence or multiple sequence alignment and perform the search in a profile database of choice. The output is structured similar to that of BLAST, with the list of detected homologs sorted by E-value and followed by profile–profile alignments. The front page allows the user to adjust multiple options of input processing and output formatting, as well as search settings, including the relative weights assigned to the three types of assisting information

Estimates of statistical significance for comparison of individual positions in multiple sequence alignments

BACKGROUND: Profile-based analysis of multiple sequence alignments (MSA) allows for accurate comparison of protein families. Here, we address the problems of detecting statistically confident dissimilarities between (1) MSA position and a set of predicted residue frequencies, and (2) between two MSA positions. These problems are important for (i) evaluation and optimization of methods predicting residue occurrence at protein positions; (ii) detection of potentially misaligned regions in automatically produced alignments and their further refinement; and (iii) detection of sites that determine functional or structural specificity in two related families. RESULTS: For problems (1) and (2), we propose analytical estimates of P-value and apply them to the detection of significant positional dissimilarities in various experimental situations. (a) We compare structure-based predictions of residue propensities at a protein position to the actual residue frequencies in the MSA of homologs. (b) We evaluate our method by the ability to detect erroneous position matches produced by an automatic sequence aligner. (c) We compare MSA positions that correspond to residues aligned by automatic structure aligners. (d) We compare MSA positions that are aligned by high-quality manual superposition of structures. Detected dissimilarities reveal shortcomings of the automatic methods for residue frequency prediction and alignment construction. For the high-quality structural alignments, the dissimilarities suggest sites of potential functional or structural importance. CONCLUSION: The proposed computational method is of significant potential value for the analysis of protein families

Exploring dynamics of protein structure determination and homology-based prediction to estimate the number of superfamilies and folds

BACKGROUND: As tertiary structure is currently available only for a fraction of known protein families, it is important to assess what parts of sequence space have been structurally characterized. We consider protein domains whose structure can be predicted by sequence similarity to proteins with solved structure and address the following questions. Do these domains represent an unbiased random sample of all sequence families? Do targets solved by structural genomic initiatives (SGI) provide such a sample? What are approximate total numbers of structure-based superfamilies and folds among soluble globular domains? RESULTS: To make these assessments, we combine two approaches: (i) sequence analysis and homology-based structure prediction for proteins from complete genomes; and (ii) monitoring dynamics of the assigned structure set in time, with the accumulation of experimentally solved structures. In the Clusters of Orthologous Groups (COG) database, we map the growing population of structurally characterized domain families onto the network of sequence-based connections between domains. This mapping reveals a systematic bias suggesting that target families for structure determination tend to be located in highly populated areas of sequence space. In contrast, the subset of domains whose structure is initially inferred by SGI is similar to a random sample from the whole population. To accommodate for the observed bias, we propose a new non-parametric approach to the estimation of the total numbers of structural superfamilies and folds, which does not rely on a specific model of the sampling process. Based on dynamics of robust distribution-based parameters in the growing set of structure predictions, we estimate the total numbers of superfamilies and folds among soluble globular proteins in the COG database. CONCLUSION: The set of currently solved protein structures allows for structure prediction in approximately a third of sequence-based domain families. The choice of targets for structure determination is biased towards domains with many sequence-based homologs. The growing SGI output in the future should further contribute to the reduction of this bias. The total number of structural superfamilies and folds in the COG database are estimated as ~4000 and ~1700. These numbers are respectively four and three times higher than the numbers of superfamilies and folds that can currently be assigned to COG proteins

A tale of two ferredoxins: sequence similarity and structural differences

BACKGROUND: Sequence similarity between proteins is usually considered a reliable indicator of homology. Pyruvate-ferredoxin oxidoreductase and quinol-fumarate reductase contain ferredoxin domains that bind [Fe-S] clusters and are involved in electron transport. Profile-based methods for sequence comparison, such as PSI-BLAST and HMMer, suggest statistically significant similarity between these domains. RESULTS: The sequence similarity between these ferredoxin domains resides in the area of the [Fe-S] cluster-binding sites. Although overall folds of these ferredoxins bear no obvious similarity, the regions of sequence similarity display a remarkable local structural similarity. These short regions with pronounced sequence motifs are incorporated in completely different structural environments. In pyruvate-ferredoxin oxidoreductase (bacterial ferredoxin), the hydrophobic core of the domain is completed by two β-hairpins, whereas in quinol-fumarate reductase (α-helical ferredoxin), the cluster-binding motifs are part of a larger all-α-helical globin-like fold core. CONCLUSION: Functionally meaningful sequence similarity may sometimes be reflected only in local structural similarity, but not in global fold similarity. If detected and used naively, such similarities may lead to incorrect fold predictions

X-chromosome hyperactivation in mammals via nonlinear relationships between chromatin states and transcription

Dosage compensation in mammals occurs at two levels. In addition to balancing X-chromosome dosage between males and females via X-inactivation, mammals also balance dosage of Xs and autosomes. It has been proposed that X-autosome equalization occurs by upregulation of Xa (active X). To investigate mechanism, we perform allele-specific ChIP-seq for chromatin epitopes and analyze RNA-seq data. The hypertranscribed Xa demonstrates enrichment of active chromatin marks relative to autosomes. We derive predictive models for relationships among POL-II, active mark densities, and gene expression, and suggest that Xa upregulation involves increased transcription initiation and elongation. Enrichment of active marks on Xa does not scale proportionally with transcription output, a disparity explained by nonlinear quantitative dependencies among active histone marks, POL-II occupancy, and transcription. Significantly, the trend of nonlinear upregulation also occurs on autosomes. Thus, Xa upregulation involves combined increases of active histone marks and POL-II occupancy, without invoking X-specific dependencies between chromatin states and transcription

COMPASS server for remote homology inference

COMPASS is a method for homology detection and local alignment construction based on the comparison of multiple sequence alignments (MSAs). The method derives numerical profiles from given MSAs, constructs local profile-profile alignments and analytically estimates E-values for the detected similarities. Until now, COMPASS was only available for download and local installation. Here, we present a new web server featuring the latest version of COMPASS, which provides (i) increased sensitivity and selectivity of homology detection; (ii) longer, more complete alignments; and (iii) faster computational speed. After submission of the query MSA or single sequence, the server performs searches versus a user-specified database. The server includes detailed and intuitive control of the search parameters. A flexible output format, structured similarly to BLAST and PSI-BLAST, provides an easy way to read and analyze the detected profile similarities. Brief help sections are available for all input parameters and output options, along with detailed documentation. To illustrate the value of this tool for protein structure-functional prediction, we present two examples of detecting distant homologs for uncharacterized protein families. Available at http://prodata.swmed.edu/compas

Considering scores between unrelated proteins in the search database improves profile comparison

<p>Abstract</p> <p>Background</p> <p>Profile-based comparison of multiple sequence alignments is a powerful methodology for the detection remote protein sequence similarity, which is essential for the inference and analysis of protein structure, function, and evolution. Accurate estimation of statistical significance of detected profile similarities is essential for further development of this methodology. Here we analyze a novel approach to estimate the statistical significance of profile similarity: the explicit consideration of background score distributions for each database template (subject).</p> <p>Results</p> <p>Using a simple scheme to combine and analytically approximate query- and subject-based distributions, we show that (i) inclusion of background distributions for the subjects increases the quality of homology detection; (ii) this increase is higher when the distributions are based on the scores to all known non-homologs of the subject rather than a small calibration subset of the database representatives; and (iii) these all known non-homolog distributions of scores for the subject make the dominant contribution to the improved performance: adding the calibration distribution of the query has a negligible additional effect.</p> <p>Conclusion</p> <p>The construction of distributions based on the complete sets of non-homologs for each subject is particularly relevant in the setting of structure prediction where the database consists of proteins with solved 3D structure (PDB, SCOP, CATH, etc.) and therefore structural relationships between proteins are known. These results point to a potential new direction in the development of more powerful methods for remote homology detection.</p

Accurate statistical model of comparison between multiple sequence alignments

Comparison of multiple protein sequence alignments (MSA) reveals unexpected evolutionary relations between protein families and leads to exciting predictions of spatial structure and function. The power of MSA comparison critically depends on the quality of statistical model used to rank the similarities found in a database search, so that biologically relevant relationships are discriminated from spurious connections. Here, we develop an accurate statistical description of MSA comparison that does not originate from conventional models of single sequence comparison and captures essential features of protein families. As a final result, we compute E-values for the similarity between any two MSA using a mathematical function that depends on MSA lengths and sequence diversity. To develop these estimates of statistical significance, we first establish a procedure for generating realistic alignment decoys that reproduce natural patterns of sequence conservation dictated by protein secondary structure. Second, since similarity scores between these alignments do not follow the classic Gumbel extreme value distribution, we propose a novel distribution that yields statistically perfect agreement with the data. Third, we apply this random model to database searches and show that it surpasses conventional models in the accuracy of detecting remote protein similarities

A comprehensive system for evaluation of remote sequence similarity detection

<p>Abstract</p> <p>Background</p> <p>Accurate and sensitive performance evaluation is crucial for both effective development of better structure prediction methods based on sequence similarity, and for the comparative analysis of existing methods. Up to date, there has been no satisfactory comprehensive evaluation method that (i) is based on a large and statistically unbiased set of proteins with clearly defined relationships; and (ii) covers all performance aspects of sequence-based structure predictors, such as sensitivity and specificity, alignment accuracy and coverage, and structure template quality.</p> <p>Results</p> <p>With the aim of designing such a method, we (i) select a statistically balanced set of divergent protein domains from SCOP, and define similarity relationships for the majority of these domains by complementing the best of information available in SCOP with a rigorous SVM-based algorithm; and (ii) develop protocols for the assessment of similarity detection and alignment quality from several complementary perspectives. The evaluation of similarity detection is based on ROC-like curves and includes several complementary approaches to the definition of true/false positives. Reference-dependent approaches use the 'gold standard' of pre-defined domain relationships and structure-based alignments. Reference-independent approaches assess the quality of structural match predicted by the sequence alignment, with respect to the whole domain length (global mode) or to the aligned region only (local mode). Similarly, the evaluation of alignment quality includes several reference-dependent and -independent measures, in global and local modes. As an illustration, we use our benchmark to compare the performance of several methods for the detection of remote sequence similarities, and show that different aspects of evaluation reveal different properties of the evaluated methods, highlighting their advantages, weaknesses, and potential for further development.</p> <p>Conclusion</p> <p>The presented benchmark provides a new tool for a statistically unbiased assessment of methods for remote sequence similarity detection, from various complementary perspectives. This tool should be useful both for users choosing the best method for a given purpose, and for developers designing new, more powerful methods. The benchmark set, reference alignments, and evaluation codes can be downloaded from <url>ftp://iole.swmed.edu/pub/evaluation/</url>.</p