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PubMed2004

Porter: a new, accurate server for protein secondary structure prediction.

UNLABELLED: Porter is a new system for protein secondary structure prediction in three classes. Porter relies on bidirectional recurrent neural networks with shortcut connections, accurate coding of input profiles obtained from multiple sequence alignments, second stage filtering by recurrent neural networks, incorporation of long range information and large-scale ensembles of predictors. Porter's accuracy, tested by rigorous 5-fold cross-validation on a large set of proteins, exceeds 79%, significantly above a copy of the state-of-the-art SSpro server, better than any system published to date. AVAILABILITY: Porter is available as a public web server at http://distill.ucd.ie/porter/ CONTACT: gianluca.pollastri@ucd.ie.

Algorithms↗

PubMed2005

REVCOM: a robust Bayesian method for evolutionary rate estimation.

MOTIVATION: Evolutionary conservation estimated from a multiple sequence alignment is a powerful indicator of the functional significance of a residue and helps to predict active sites, ligand binding sites, and protein interaction interfaces. Many algorithms that calculate conservation work well, provided an accurate and balanced alignment is used. However, such a strong dependence on the alignment makes the results highly variable. We attempted to improve the conservation prediction algorithm by making it more robust and less sensitive to (1) local alignment errors, (2) overrepresentation of sequences in some branches and (3) occasional presence of unrelated sequences. RESULTS: A novel method is presented for robust constrained Bayesian estimation of evolutionary rates that avoids overfitting independent rates and satisfies the above requirements. The method is evaluated and compared with an entropy-based conservation measure on a set of 1494 protein interfaces. We demonstrated that approximately 62% of the analyzed protein interfaces are more conserved than the remaining surface at the 5% significance level. A consistent method to incorporate alignment reliability is proposed and demonstrated to reduce arbitrary variation of calculated rates upon inclusion of distantly related or unrelated sequences into the alignment.

Algorithms↗

PubMed2005

Prediction of the phenotypic effects of non-synonymous single nucleotide polymorphisms using structural and evolutionary information.

MOTIVATION: There has been great expectation that the knowledge of an individual's genotype will provide a basis for assessing susceptibility to diseases and designing individualized therapy. Non-synonymous single nucleotide polymorphisms (nsSNPs) that lead to an amino acid change in the protein product are of particular interest because they account for nearly half of the known genetic variations related to human inherited diseases. To facilitate the identification of disease-associated nsSNPs from a large number of neutral nsSNPs, it is important to develop computational tools to predict the phenotypic effects of nsSNPs. RESULTS: We prepared a training set based on the variant phenotypic annotation of the Swiss-Prot database and focused our analysis on nsSNPs having homologous 3D structures. Structural environment parameters derived from the 3D homologous structure as well as evolutionary information derived from the multiple sequence alignment were used as predictors. Two machine learning methods, support vector machine and random forest, were trained and evaluated. We compared the performance of our method with that of the SIFT algorithm, which is one of the best predictive methods to date. An unbiased evaluation study shows that for nsSNPs with sufficient evolutionary information (with not <10 homologous sequences), the performance of our method is comparable with the SIFT algorithm, while for nsSNPs with insufficient evolutionary information (<10 homologous sequences), our method outperforms the SIFT algorithm significantly. These findings indicate that incorporating structural information is critical to achieving good prediction accuracy when sufficient evolutionary information is not available. AVAILABILITY: The codes and curated dataset are available at http://compbio.utmem.edu/snp/dataset/

Algorithms↗

PubMed2005

A comprehensive and non-redundant database of protein domain movements.

MOTIVATION: The current DynDom database of protein domain motions is a user-created database that suffers from selectivity and redundancy. The aim of the analysis presented here was to overcome both these limitations and to produce both a comprehensive and a non-redundant description of domain movements from structures stored in the current protein data bank. RESULTS: A multi-step procedure is applied that starts with grouping proteins in the structural databank into families based on sequence similarity. Multiple sequence alignment, conformational clustering and a dimensional clustering method based on the Gram-Schmidt algorithm are applied to members of each family to remove dynamic redundancy in their domain movements. Representative domain movements are described in terms of domains, hinge axes and hinge-bending residues using the DynDom program. The results show that within an average family of 11.5 members, there are on average only 1.31 different domain movements indicating a high redundancy in the movements these structures represent. This verifies earlier findings that domain movements are usually highly controlled. Despite the removal of this considerable redundancy, the process has resulted in double the number of domain movements stored in the user-created database. The data are organized in a relational database with a web-interface. AVAILABILITY: The database can be browsed and searched at http://www.cmp.uea.ac.uk/dyndom CONTACT: sjh@cmp.uea.ac.uk.

Algorithms↗

PubMed2005

P-cats: prediction of catalytic residues in proteins from their tertiary structures.

UNLABELLED: P-cats is a web server that predicts the catalytic residues in proteins from the atomic coordinates. P-cats receives a coordinate file of the tertiary structure and sends out analytical results via e-mail. The reply contains a summary and two URLs to allow the user to examine the conserved residues: one for interactive images of the prediction results and the other for a graphical view of the multiple sequence alignment. AVAILABILITY: P-cats is freely available at http://p-cats.hgc.jp/p-cats CONTACT: kino@ims.u-tokyo.ac.jp

Algorithms↗

PubMed2005

Using information theory to search for co-evolving residues in proteins.

MOTIVATION: Some functionally important protein residues are easily detected since they correspond to conserved columns in a multiple sequence alignment (MSA). However important residues may also mutate, with compensatory mutations occurring elsewhere in the protein, which serve to preserve or restore functionality. It is difficult to distinguish these co-evolving sites from other non-conserved sites. RESULTS: We used Mutual Information (MI) to identify co-evolving positions. Using in silico evolved MSAs, we examined the effects of the number of sequences, the size of amino acid alphabet and the mutation rate on two sources of background MI: finite sample size effects and phylogenetic influence. We then assessed the performance of various normalizations of MI in enhancing detection of co-evolving positions and found that normalization by the pair entropy was optimal. Real protein alignments were analyzed and co-evolving isolated pairs were often found to be in contact with each other. AVAILABILITY: All data and program files can be found at http://www.biochem.uwo.ca/cgi-bin/CDD/index.cgi

Amino Acid Sequence↗

PubMed2005

ConFind: a robust tool for conserved sequence identification.

SUMMARY: ConFind (conserved region finder) identifies regions of conservation in multiple sequence alignments that can serve as diagnostic targets. Designed to work with a large number of closely related, highly variable sequences, ConFind provides robust handling of alignments containing partial sequences and ambiguous characters. Conserved regions are defined in terms of minimum region length, maximum informational entropy (variability) per position, number of exceptions allowed to the maximum entropy criterion and the minimum number of sequences that must contain a non-ambiguous character at a position to be considered for inclusion in a conserved region. Comparison of the calculated entropy for an alignment of 95 influenza A hemagglutinin sequences with random deletions results in a 98% reduction in the average error in ConFind relative to the 'Find Conserved Regions' option in BioEdit. REQUIREMENTS: ConFind requires Python 2.3, but Python 2.4 or an upgrade of the optparse module to Optik 1.5 is suggested. The program is known to run under Linux and DOS.

Algorithms↗

PubMed2005

Prediction of functional specificity determinants from protein sequences using log-likelihood ratios.

MOTIVATION: A number of methods have been developed to predict functional specificity determinants in protein families based on sequence information. Most of these methods rely on pre-defined functional subgroups. Manual subgroup definition is difficult because of the limited number of experimentally characterized subfamilies with differing specificity, while automatic subgroup partitioning using computational tools is a non-trivial task and does not always yield ideal results. RESULTS: We propose a new approach SPEL (specificity positions by evolutionary likelihood) to detect positions that are likely to be functional specificity determinants. SPEL, which does not require subgroup definition, takes a multiple sequence alignment of a protein family as the only input, and assigns a P-value to every position in the alignment. Positions with low P-values are likely to be important for functional specificity. An evolutionary tree is reconstructed during the calculation, and P-value estimation is based on a random model that involves evolutionary simulations. Evolutionary log-likelihood is chosen as a measure of amino acid distribution at a position. To illustrate the performance of the method, we carried out a detailed analysis of two protein families (LacI/PurR and G protein alpha subunit), and compared our method with two existing methods (evolutionary trace and mutual information based). All three methods were also compared on a set of protein families with known ligand-bound structures. AVAILABILITY: SPEL is freely available for non-commercial use. Its pre-compiled versions for several platforms and alignments used in this work are available at ftp://iole.swmed.edu/pub/SPEL/

Algorithms↗

PubMed2005

SEAN: SNP prediction and display program utilizing EST sequence clusters.

SEAN is an application that predicts single nucleotide polymorphisms (SNPs) using multiple sequence alignments produced from expressed sequence tag (EST) clusters. The algorithm uses rules of sequence identity and SNP abundance to determine the quality of the prediction. A Java viewer is provided to display the EST alignments and predicted SNPs.

Algorithms↗

PubMed2006

Recco: recombination analysis using cost optimization.

MOTIVATION: Recombination plays an important role in the evolution of many pathogens, such as HIV or malaria. Despite substantial prior work, there is still a pressing need for efficient and effective methods of detecting recombination and analyzing recombinant sequences. RESULTS: We introduce Recco, a novel fast method that, given a multiple sequence alignment, scores the cost of obtaining one of the sequences from the others by mutation and recombination. The algorithm comes with an illustrative visualization tool for locating recombination breakpoints. We analyze the sequence alignment with respect to all choices of the parameter alpha weighting recombination cost against mutation cost. The analysis of the resulting cost curve yields additional information as to which sequence might be recombinant. On random genealogies Recco is comparable in its power of detecting recombination with the algorithm Geneconv (Sawyer, 1989). For specific relevant recombination scenarios Recco significantly outperforms Geneconv.

Algorithms↗

PubMed2006

Phylogeny-independent detection of functional residues.

MOTIVATION: Current projects for the massive characterization of proteomes are generating protein sequences and structures with unknown function. The difficulty of experimentally determining functionally important sites calls for the development of computational methods. The first techniques, based on the search for fully conserved positions in multiple sequence alignments (MSAs), were followed by methods for locating family-dependent conserved positions. These rely on the functional classification implicit in the alignment for locating these positions related with functional specificity. The next obvious step, still scarcely explored, is to detect these positions using a functional classification different from the one implicit in the sequence relationships between the proteins. Here, we present two new methods for locating functional positions which can incorporate an arbitrary external functional classification which may or may not coincide with the one implicit in the MSA. The Xdet method is able to use a functional classification with an associated hierarchy or similarity between functions to locate positions related to that classification. The MCdet method uses multivariate statistical analysis to locate positions responsible for each one of the functions within a multifunctional family. RESULTS: We applied the methods to different cases, illustrating scenarios where there is a disagreement between the functional and the phylogenetic relationships, and demonstrated their usefulness for the phylogeny-independent prediction of functional positions.

Amino Acid Sequence↗

PubMed2006

DRTF: a database of rice transcription factors.

SUMMARY: DRTF contains 2025 putative transcription factors (TFs) in Oryza sativa L. ssp. indica and 2384 in ssp. japonica, distributed in 63 families, identified by computational prediction and manual curation. It includes detailed annotations of each TF including sequence features, functional domains, Gene Ontology assignment, chromosomal localization, EST and microarray expression information, as well as multiple sequence alignment of the DNA-binding domains for each TF family. The database can be browsed and searched with a user-friendly web interface. AVAILABILITY: DRTF is available at http://drtf.cbi.pku.edu.cn

Database Management Systems↗

PubMed2006

Hairpins in a Haystack: recognizing microRNA precursors in comparative genomics data.

UNLABELLED: Recently, genome-wide surveys for non-coding RNAs have provided evidence for tens of thousands of previously undescribed evolutionary conserved RNAs with distinctive secondary structures. The annotation of these putative ncRNAs, however, remains a difficult problem. Here we describe an SVM-based approach that, in conjunction with a non-stringent filter for consensus secondary structures, is capable of efficiently recognizing microRNA precursors in multiple sequence alignments. The software was applied to recent genome-wide RNAz surveys of mammals, urochordates, and nematodes. AVAILABILITY: The program RNAmicro is available as source code and can be downloaded from http://www.bioinf.uni-leipzig/Software/RNAmicro.

Algorithms↗

PubMed2006

GARD: a genetic algorithm for recombination detection.

MOTIVATION: Phylogenetic and evolutionary inference can be severely misled if recombination is not accounted for, hence screening for it should be an essential component of nearly every comparative study. The evolution of recombinant sequences can not be properly explained by a single phylogenetic tree, but several phylogenies may be used to correctly model the evolution of non-recombinant fragments. RESULTS: We developed a likelihood-based model selection procedure that uses a genetic algorithm to search multiple sequence alignments for evidence of recombination breakpoints and identify putative recombinant sequences. GARD is an extensible and intuitive method that can be run efficiently in parallel. Extensive simulation studies show that the method nearly always outperforms other available tools, both in terms of power and accuracy and that the use of GARD to screen sequences for recombination ensures good statistical properties for methods aimed at detecting positive selection. AVAILABILITY: Freely available http://www.datamonkey.org/GARD/

Algorithms↗

PubMed2006

PartTree: an algorithm to build an approximate tree from a large number of unaligned sequences.

MOTIVATION: To construct a multiple sequence alignment (MSA) of a large number (> approximately 10,000) of sequences, the calculation of a guide tree with a complexity of O(N2) to O(N3), where N is the number of sequences, is the most time-consuming process. RESULTS: To overcome this limitation, we have developed an approximate algorithm, PartTree, to construct a guide tree with an average time complexity of O(N log N). The new MSA method with the PartTree algorithm can align approximately 60,000 sequences in several minutes on a standard desktop computer. The loss of accuracy in MSA caused by this approximation was estimated to be several percent in benchmark tests using Pfam. AVAILABILITY: The present algorithm has been implemented in the MAFFT sequence alignment package (http://align.bmr.kyushu-u.ac.jp/mafft/software/). SUPPLEMENTARY INFORMATION: Supplementary information is available at Bioinformatics online.

Algorithms↗

PubMed1998

Direct link between cytokine activity and a catalytic site for macrophage migration inhibitory factor.

Macrophage migration inhibitory factor (MIF) is a secreted protein that activates macrophages, neutrophils and T cells, and is implicated in sepsis, adult respiratory distress syndrome and rheumatoid arthritis. The mechanism of MIF function, however, is unknown. The three-dimensional structure of MIF is unlike that of any other cytokine, but bears striking resemblance to three microbial enzymes, two of which possess an N-terminal proline that serves as a catalytic base. Human MIF also possesses an N-terminal proline (Pro-1) that is invariant among all known homologues. Multiple sequence alignment of these MIF homologues reveals additional invariant residues that span the entire polypeptide but are in close proximity to the N-terminal proline in the folded protein. We find that p-hydroxyphenylpyruvate, a catalytic substrate of MIF, binds to the N-terminal region and interacts with Pro-1. Mutation of Pro-1 to a glycine substantially reduces the catalytic and cytokine activity of MIF. We suggest that the underlying biological activity of MIF may be based on an enzymatic reaction. The identification of the active site should facilitate the development of structure-based inhibitors.

Amino Acid Sequence↗

PubMed2025

Inference of Cytochrome P450 Evolutionary History Using Structural and Physicochemical Metrics.

Cytochrome P450s are a superfamily of heme-binding monooxygenases involved with the detoxification of intrinsic and extrinsic toxins. They are near ubiquitous within biological domains and are found in all domains. Members of families within the superfamily are defined based on amino acid identity thresholds, with thresholds as low as 40% in some families. Relationships among Cytochrome P450 families have proven elusive due to sub-Twilight Zone interfamily identities (<30%) that result in poor multiple sequence alignment quality and thus low levels of support for downstream phylogenetic reconstructions. Despite the low identities, Cytochrome P450 structures are remarkably well conserved both within and among families. In such cases, structural phylogenetics has the potential to unveil elusive relationships because the selectively favored physicochemical properties giving rise to the structure and function of the proteins persist despite sequence-level divergence. Recently, in two separate publications, we demonstrated that by utilizing physicochemical vectors, dynamic time warping, and hierarchical clustering (PCDTW), large swaths of protein domain families and betacoronavirus receptor-binding domain clades were congruent with validated functional/structural relationships. These were important findings because anomalous sequence alignment-based maximum likelihood phylogenetic findings, which were not congruent with the known functional relationships, were resolved. That also validated the use of physicochemical vectors in making inferences about structural/functional homology. Additionally, it illuminated that the same methods might be applied to other protein families with relationships that are difficult to resolve from sequence data alone. Herein, we used Molecular Weight and Hydrophobicity Physicochemical Dynamic Time Warping (MWHP PCDTW) along with structural and sequence alignment-based phylogenetic methodologies to analyze all of the Cytochrome P450s found both in the high-fidelity Structural Classificaction of Proteins (SCOP) database and the reviewed sequences with both experimentally resolved and de novo predicted structures in the Protein Data Bank and the AlphaFold (AF) Protein Structure Database, respectively. We compared the resulting phylogenetic topologies and found that in some cases, structure-based methods may be less able to resolve random/convergent similarity than physicochemical and sequence-based methodologies. This finding agrees with previous findings that demonstrate the usefulness of physicochemical properties in resolving both random structural similarity and potentially convergent relationships.

Cytochrome P-450 Enzyme System↗

PubMed2026

Reducing haystacks to needles - ViralClust: A Nextflow pipeline to cluster viral sequences.

BACKGROUND: The rapid accumulation of viral genome sequences presents major challenges for downstream analysis tools, including tools for multiple sequence alignments, phylogeny, and genome/alignment visualization, due to computational constraints and sampling biases caused by outbreak-driven over-representation. Selecting representative genomes through clustering offers a principled alternative to random subsampling, yet choosing appropriate clustering strategies remains non-trivial and context-dependent. RESULTS: Here, we present ViralClust, a modular Nextflow pipeline for bias-aware representative selection from large viral genome datasets. ViralClust integrates five distinct clustering algorithms (CD-HIT-EST, SUMACLUST, VSEARCH, MMSeqs2, and HDBSCAN) within a unified workflow, enabling direct comparison of clustering outcomes and flexible adaptation to diverse biological questions, considering a balanced phylogenetic distribution of the selected sequences. We evaluated ViralClust on six RNA and DNA virus datasets ranging from 632 to 156,586 sequences and spanning genome lengths from 890 to 197,185 nucleotides. Across all datasets, clustering reduced dataset size by ~95 % or more while preserving genetic diversity across species, genera, and families, and effectively mitigating biases introduced by outbreaks, partial genomes, and sequence orientation artifacts. CONCLUSIONS: By supporting whole-genome clustering and scalable representative selection, ViralClust enables efficient and reproducible downstream analyses that would otherwise be computationally infeasible. Rather than offering a prescriptive, guided analysis engine, our framework functions as a flexible comparative collection of complementary strategies, allowing users to empirically evaluate trade-offs and choose the ideal method tailored to their specific analytical endpoints.

Bioinformatics↗