Method For Remote Homology Detection Research Articles

CHLAMYDIA TRACHOMATIS EXPRESSES PHYLOGENETICALLY HOMOLOGOUS PROTEINS TO HUMAN PAPILLOMA VIRUS TYPE 16: INFERENCES FOR ORAL AND OROPHARYNGEAL SQUAMOUS CELL CARCINOMA

Background Oral and oropharyngeal squamous cell carcinoma have been linked to infection by human papilloma virus (HPV) types 16 and 18. They produce proteins E6 and E7 that interfere with p53 and pRb1 proteins and hence support oncogenesis. HPV infection is typically via oro-genital contact and other organisms cohabiting the genitalia may have had sufficient time for a genetic exchange with HPV oncoproteins and hence develop oncogenic potential also within the oral cavity. Objective Our aim was to search for organisms with homologous match for HPV 16 E6/E7 protein sequences. Methods HPV 16 sequences were obtained from the NCBI database. E6 and E7 homologous sequences with accession numbers >ACQ90217 and >ACQ90218 were retrieved through BLAST search. The SWISS-MODEL template library was searched with BLAST and HHBlits for evolutionary related structures matching the target sequence. Utilizing the MPI Bioinformatics remote homology detection method linked with the UniProt database, best matches of E6 and E7 protein sequences were retrieved. Multiple sequence alignment was done and ProMod3 was used to build a protein model for E7. Results HPV 16 E7 protein and Chlamydia trachomatis homologous protein sequences were the most closely related from our bioinformatic search. Conclusions This similarity between C. trachomatis proteins and HPV 16 E7 oncogenic proteins could be because both organisms inhabit similar mucosae and have had considerable time to exchange genes. We postulate that a possible co-infection of C. trachomatis may be implicated in the development of oral and oropharyngeal squamous cell carcinoma as documented with cervical cancers. Oral and oropharyngeal squamous cell carcinoma have been linked to infection by human papilloma virus (HPV) types 16 and 18. They produce proteins E6 and E7 that interfere with p53 and pRb1 proteins and hence support oncogenesis. HPV infection is typically via oro-genital contact and other organisms cohabiting the genitalia may have had sufficient time for a genetic exchange with HPV oncoproteins and hence develop oncogenic potential also within the oral cavity. Our aim was to search for organisms with homologous match for HPV 16 E6/E7 protein sequences. HPV 16 sequences were obtained from the NCBI database. E6 and E7 homologous sequences with accession numbers >ACQ90217 and >ACQ90218 were retrieved through BLAST search. The SWISS-MODEL template library was searched with BLAST and HHBlits for evolutionary related structures matching the target sequence. Utilizing the MPI Bioinformatics remote homology detection method linked with the UniProt database, best matches of E6 and E7 protein sequences were retrieved. Multiple sequence alignment was done and ProMod3 was used to build a protein model for E7. HPV 16 E7 protein and Chlamydia trachomatis homologous protein sequences were the most closely related from our bioinformatic search. This similarity between C. trachomatis proteins and HPV 16 E7 oncogenic proteins could be because both organisms inhabit similar mucosae and have had considerable time to exchange genes. We postulate that a possible co-infection of C. trachomatis may be implicated in the development of oral and oropharyngeal squamous cell carcinoma as documented with cervical cancers.

Using Grey Model to Predict Protein Remote Homologous Family

Protein remote homology detection is the most basic and core problems of protein structure and function research. The purpose of protein remote homology detection to detect the remote evolutionary relationship between proteins by computation methods. At present, there are many methods for remote homology detection of proteins, but some methods are not very effective. In bioinformatics, it is urgent to further improve the performance of protein remote homology detection. In this study, we propose a new model called PHom-GRA to detect protein remote homology, which is the integration of various ranking methods via using grey relational analysis. Our experiment constructs benchmark dataset with lower homology, in which any two proteins have not more than 40% identity or homology. We achieve an ROC1 score of 0.7372 and an ROC50 score of 0.7968 in jack-knife test.

Remote-3DD: un nuevo método para la detección de homólogos remotos que usa propiedades fisicoquímicas

En este artículo se presenta un nuevo método para la detección de homólogos remotos, llamado remote-3DD, que combina mapas de contacto predichos y una distribución de los valores en las matrices de interacción. Los mapas de contacto predichos son una aproximación de la forma 3D de proteína que se puede obtener a partir de su estructura primaria. Por su parte, una matriz de interacción permite representar una proteína a partir de las propiedades fisicoquímicas de los aminoácidos que la conforman. Remote-3DD se propone como una estrategia para mejorar la exactitud del método remote-C3D en el cual se utilizan solamente mapas de contacto. La hipótesis que se plantea en este artículo es que se puede mejorar la exactitud del método remote-C3D al incorporar las distribuciones de la matriz de interacción. Los resultados de las pruebas muestran que el método remote-3DD alcanza una exactitud mayor que los métodos basados en composición y en algunos casos una exactitud comparable con los métodos basados en perfiles. Además, las pruebas permiten demostrar que el método remote-3DD, en general, presenta exactitudes mayores que el método remote-C3D cuando se utiliza la misma cantidad de modelos y tamaños de submatrices.

Comprehensive classification of the PIN domain-like superfamily.

PIN-like domains constitute a widespread superfamily of nucleases, diverse in terms of the reaction mechanism, substrate specificity, biological function and taxonomic distribution. Proteins with PIN-like domains are involved in central cellular processes, such as DNA replication and repair, mRNA degradation, transcription regulation and ncRNA maturation. In this work, we identify and classify the most complete set of PIN-like domains to provide the first comprehensive analysis of sequence–structure–function relationships within the whole PIN domain-like superfamily. Transitive sequence searches using highly sensitive methods for remote homology detection led to the identification of several new families, including representatives of Pfam (DUF1308, DUF4935) and CDD (COG2454), and 23 other families not classified in the public domain databases. Further sequence clustering revealed relationships between individual sequence clusters and showed heterogeneity within some families, suggesting a possible functional divergence. With five structural groups, 70 defined clusters, over 100,000 proteins, and broad biological functions, the PIN domain-like superfamily constitutes one of the largest and most diverse nuclease superfamilies. Detailed analyses of sequences and structures, domain architectures, and genomic contexts allowed us to predict biological function of several new families, including new toxin-antitoxin components, proteins involved in tRNA/rRNA maturation and transcription/translation regulation.

Rapid and enhanced remote homology detection by cascading hidden Markov model searches in sequence space.

In the post-genomic era, automatic annotation of protein sequences using computational homology-based methods is highly desirable. However, often protein sequences diverge to an extent where detection of homology and automatic annotation transfer is not straightforward. Sophisticated approaches to detect such distant relationships are needed. We propose a new approach to identify deep evolutionary relationships of proteins to overcome shortcomings of the available methods. We have developed a method to identify remote homologues more effectively from any protein sequence database by using several cascading events with Hidden Markov Models (C-HMM). We have implemented clustering of hits and profile generation of hit clusters to effectively reduce the computational timings of the cascaded sequence searches. Our C-HMM approach could cover 94, 83 and 40% coverage at family, superfamily and fold levels, respectively, when applied on diverse protein folds. We have compared C-HMM with various remote homology detection methods and discuss the trade-offs between coverage and false positives. A standalone package implemented in Java along with a detailed documentation can be downloaded from https://github.com/RSLabNCBS/C-HMM SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. mini@ncbs.res.in.

The Phyre2 web portal for protein modeling, prediction and analysis.

Phyre2 is a suite of tools available on the web to predict and analyze protein structure, function and mutations. The focus of Phyre2 is to provide biologists with a simple and intuitive interface to state-of-the-art protein bioinformatics tools. Phyre2 replaces Phyre, the original version of the server for which we previously published a paper in Nature Protocols. In this updated protocol, we describe Phyre2, which uses advanced remote homology detection methods to build 3D models, predict ligand binding sites and analyze the effect of amino acid variants (e.g., nonsynonymous SNPs (nsSNPs)) for a user's protein sequence. Users are guided through results by a simple interface at a level of detail they determine. This protocol will guide users from submitting a protein sequence to interpreting the secondary and tertiary structure of their models, their domain composition and model quality. A range of additional available tools is described to find a protein structure in a genome, to submit large number of sequences at once and to automatically run weekly searches for proteins that are difficult to model. The server is available at http://www.sbg.bio.ic.ac.uk/phyre2. A typical structure prediction will be returned between 30 min and 2 h after submission.

REPERTOIRE OF PROTEIN KINASES ENCODED IN THE GENOME OF ZEBRAFISH SHOWS REMARKABLY LARGE POPULATION OF PIM KINASES

In recent times, zebrafish has garnered lot of popularity as model organism to study human cancers. Despite high evolutionary divergence from humans, zebrafish develops almost all types of human tumors when induced. However, mechanistic details of tumor formation have remained largely unknown. Present study is aimed at analysis of repertoire of kinases in zebrafish proteome to provide insights into various cellular components. Annotation using highly sensitive remote homology detection methods revealed "substantial expansion" of Ser/Thr/Tyr kinase family in zebrafish compared to humans, constituting over 3% of proteome. Subsequent classification of kinases into subfamilies revealed presence of large number of CAMK group of kinases, with massive representation of PIM kinases, important for cell cycle regulation and growth. Extensive sequence comparison between human and zebrafish PIM kinases revealed high conservation of functionally important residues with a few organism specific variations. There are about 300 PIM kinases in zebrafish kinome, while human genome codes for only about 500 kinases altogether. PIM kinases have been implicated in various human cancers and are currently being targeted to explore their therapeutic potentials. Hence, in depth analysis of PIM kinases in zebrafish has opened up new avenues of research to verify the model organism status of zebrafish.

Remote homology detection incorporating the context of physicochemical properties

A new method for remote protein homology detection, called support vector machine incorporating the context of physicochemical properties (SVM-CP), is presented. Recent discriminative methods are based on concatenating information extracted from each protein by considering several physicochemical properties. We show that there are physicochemical properties that reflect the functional or structural characteristics of each specific protein family, but there are also some physicochemical properties that affect the accuracy of the classification techniques. The research highlights the importance of the selection of physicochemical properties in remote homology detection. Most of the methods slide a window over every protein sequence to extract physicochemical information. This extraction is usually performed by giving the same importance to every value in the window, i.e., averaging the physicochemical values in the observation window. SVM-CP takes into account that every residue in a sliding window has a different weight, which reflects the importance or contribution to the representative value of the window. The SVM-CP method reaches a receiver operating characteristic (ROC) score of 0.93462, which is the highest value for a remote homology detection method based on the sequence composition information.

Confidence-Guided Local Structure Prediction with HHfrag

We present a method to assess the reliability of local structure prediction from sequence. We introduce a greedy algorithm for filtering and enrichment of dynamic fragment libraries, compiled with remote-homology detection methods such as HHfrag. After filtering false hits at each target position, we reduce the fragment library to a minimal set of representative fragments, which are guaranteed to have correct local structure in regions of detectable conservation. We demonstrate that the location of conserved motifs in a protein sequence can be predicted by examining the recurrence and structural homogeneity of detected fragments. The resulting confidence score correlates with the local RMSD of the representative fragments and allows us to predict torsion angles from sequence with better accuracy compared to existing machine learning methods.

Learning virulent proteins from integrated query networks

BackgroundMethods of weakening and attenuating pathogens’ abilities to infect and propagate in a host, thus allowing the natural immune system to more easily decimate invaders, have gained attention as alternatives to broad-spectrum targeting approaches. The following work describes a technique to identifying proteins involved in virulence by relying on latent information computationally gathered across biological repositories, applicable to both generic and specific virulence categories.ResultsA lightweight method for data integration is used, which links information regarding a protein via a path-based query graph. A method of weighting is then applied to query graphs that can serve as input to various statistical classification methods for discrimination, and the combined usage of both data integration and learning methods are tested against the problem of both generalized and specific virulence function prediction.ConclusionsThis approach improves coverage of functional data over a protein. Moreover, while depending largely on noisy and potentially non-curated data from public sources, we find it outperforms other techniques to identification of general virulence factors and baseline remote homology detection methods for specific virulence categories.

Sequence, structure and functional diversity of PD-(D/E)XK phosphodiesterase superfamily

Proteins belonging to PD-(D/E)XK phosphodiesterases constitute a functionally diverse superfamily with representatives involved in replication, restriction, DNA repair and tRNA–intron splicing. Their malfunction in humans triggers severe diseases, such as Fanconi anemia and Xeroderma pigmentosum. To date there have been several attempts to identify and classify new PD-(D/E)KK phosphodiesterases using remote homology detection methods. Such efforts are complicated, because the superfamily exhibits extreme sequence and structural divergence. Using advanced homology detection methods supported with superfamily-wide domain architecture and horizontal gene transfer analyses, we provide a comprehensive reclassification of proteins containing a PD-(D/E)XK domain. The PD-(D/E)XK phosphodiesterases span over 21 900 proteins, which can be classified into 121 groups of various families. Eleven of them, including DUF4420, DUF3883, DUF4263, COG5482, COG1395, Tsp45I, HaeII, Eco47II, ScaI, HpaII and Replic_Relax, are newly assigned to the PD-(D/E)XK superfamily. Some groups of PD-(D/E)XK proteins are present in all domains of life, whereas others occur within small numbers of organisms. We observed multiple horizontal gene transfers even between human pathogenic bacteria or from Prokaryota to Eukaryota. Uncommon domain arrangements greatly elaborate the PD-(D/E)XK world. These include domain architectures suggesting regulatory roles in Eukaryotes, like stress sensing and cell-cycle regulation. Our results may inspire further experimental studies aimed at identification of exact biological functions, specific substrates and molecular mechanisms of reactions performed by these highly diverse proteins.

The Irredundant Class Method for Remote Homology Detection of Protein Sequences

The automatic classification of protein sequences into families is of great help for the functional prediction and annotation of new proteins. In this article, we present a method called Irredundant Class that address the remote homology detection problem. The best performing methods that solve this problem are string kernels, that compute a similarity function between pairs of proteins based on their subsequence composition. We provide evidence that almost all string kernels are based on patterns that are not independent, and therefore the associated similarity scores are obtained using a set of redundant features, overestimating the similarity between the proteins. To specifically address this issue, we introduce the class of irredundant common patterns. Loosely speaking, the set of irredundant common patterns is the smallest class of independent patterns that can describe all common patterns in a pair of sequences. We present a classification method based on the statistics of these patterns, named Irredundant Class. Results on benchmark data show that the Irredundant Class outperforms most of the string kernels previously proposed, and it achieves results as good as the current state-of-the-art method Local Alignment, but using the same pairwise information only once.

On the origin of MADS-domain transcription factors

MADS-domain transcription factors are involved in signal transduction and developmental control in plants, animals and fungi. Because their diversification is linked to the origin of novelties in multicellular eukaryotes, the early evolution of MADS-domain proteins is of interest, but has remained enigmatic. Employing whole genome sequence information and remote homology detection methods, we demonstrate that the MADS domain originated from a region of topoisomerases IIA subunit A. Furthermore, we provide evidence that gene duplication occurred in the lineage that led to the MRCA of extant eukaryotes, giving rise to SRF-like and MEF2-like MADS-box genes.

Exploring genomes for glycosyltransferases

Glycosyltransferases are one of the largest and most diverse enzyme groups in Nature. They catalyse the synthesis of glycosidic linkages by the transfer of a sugar residue from a donor to an acceptor substrate. These enzymes have been classified into families on the basis of amino acid sequence similarity that are kept updated in the Carbohydrate Active enZyme database (CAZy, ). The repertoire of glycosyltransferases in genomes is believed to determine the diversity of cellular glycan structures, and current estimates suggest that for most genomes about 1% of the coding regions are glycosyltransferases. However, plants tend to have far more glycosyltransferase genes than any other organism sequenced to date, and this can be explained by the highly complex polysaccharide network that form the cell wall and also by the numerous glycosylated secondary metabolites. In recent years, various bioinformatics strategies have been used to search bacterial and plant genomes for new glycosyltransferase genes. These are based on the use of remote homology detection methods that act at the 1D, 2D, and 3D level. The combined use of methods such as profile Hidden Markov Model (HMM) and fold recognition appears to be appropriate for this class of enzyme. Chemometric tools are also particularly well suited for obtaining an overview of multivariate data and revealing hidden latent information when dealing with large and highly complex datasets.

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

BackgroundProfile-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).ResultsUsing 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.ConclusionThe 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.

PGenTHREADER and pDomTHREADER: new methods for improved protein fold recognition and superfamily discrimination

Generation of structural models and recognition of homologous relationships for unannotated protein sequences are fundamental problems in bioinformatics. Improving the sensitivity and selectivity of methods designed for these two tasks therefore has downstream benefits for many other bioinformatics applications. We describe the latest implementation of the GenTHREADER method for structure prediction on a genomic scale. The method combines profile-profile alignments with secondary-structure specific gap-penalties, classic pair- and solvation potentials using a linear combination optimized with a regression SVM model. We find this combination significantly improves both detection of useful templates and accuracy of sequence-structure alignments relative to other competitive approaches. We further present a second implementation of the protocol designed for the task of discriminating superfamilies from one another. This method, pDomTHREADER, is the first to incorporate both sequence and structural data directly in this task and improves sensitivity and selectivity over the standard version of pGenTHREADER and three other standard methods for remote homology detection.

Remote Homology Detection Using a Kernel Method that Combines Sequence and Secondary-Structure Similarity Scores

Distant evolutionary relationships between proteins with low sequence similarity are difficult to recognise by computational methods. Consequently, many sequences obtained from large-scale sequencing projects cannot be assigned to any known proteins or families despite being evolutionarily related. To boost sensitivity, various sequence-based methods have been modified to make use of the better conserved secondary structure. Most of these methods are instance-based or generative. Here, we introduce a kernel-based remote homology detection method that allows for a combination of sequence and secondary-structure similarity scores in a discriminative approach. We studied the ability of the method to predict superfamily membership as defined by the SCOP database. We show that a kernel method that combined sequence similarity scores with predicted secondary-structure similarity scores performed similar to a classifier that used scores calculated from sequences and true secondary structures, but performed better than a sequence-only based classifier and achieved a better mean than recently published results on the same data-set. It can be concluded that SVM classifiers trained to predict homology between distantly related proteins, become more accurate, if a joint sequence/secondary-structure similarity score approach is used.

Combination of Several Bioinformatics Approaches for the Identification of New Putative Glycosyltransferases inArabidopsis

Approximately 450 glycosyltransferase (GT) sequences have been already identified in the Arabidopsis genome that organize into 40 sequence-based families, but a vast majority of these gene products remain biochemically uncharacterized open reading frames. Given the complexity of the cell wall carbohydrate network, it can be inferred that some of the biosynthetic genes have not yet been identified by classical bioinformatics approaches. With the objective to identify new plant GT genes, we designed a bioinformatic strategy that is based on the use of several remote homology detection methods that act at the 1D, 2D, and 3D level. Together, these methods led to the identification of more than 150 candidate protein sequences. Among them, 20 are considered as putative glycosyltransferases that should further be investigated since known GT signatures were clearly identified.

PDBalert: automatic, recurrent remote homology tracking and protein structure prediction

BackgroundDuring 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.ResultsPDBalert 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.ConclusionPDBalert 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.

Meta-basic estimates the size of druggable human genome

We present here the estimation of the upper limit of the number of molecular targets in the human genome that represent an opportunity for further therapeutic treatment. We select around approximately 6300 human proteins that are similar to sequences of known protein targets collected from DrugBank database. Our bioinformatics study estimates the size of 'druggable' human genome to be around 20% of human proteome, i.e. the number of the possible protein targets for small-molecule drug design in medicinal chemistry. We do not take into account any toxicity prediction, the three-dimensional characteristics of the active site in the predicted 'druggable' protein families, or detailed chemical analysis of known inhibitors/drugs. Instead we rely on remote homology detection method Meta-BASIC, which is based on sequence and structural similarity. The prepared dataset of all predicted protein targets from human genome presents the unique opportunity for developing and benchmarking various in silico chemo/bio-informatics methods in the context of the virtual high throughput screening.