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The advancements in phytochemical research have offered a number of promising innovations within the field of pharmaceuticals. However, the lack of knowledge related to 3D phytochemical structures has greatly hindered the progress in the field of therapeutic drug discovery and understanding the roles of phytochemicals in biological respects. In recent years, there has been a rapid rise in the number of tools allowing the users to access the modeling methods through a broad spectrum of techniques. In most of the cases, there is a lack of experimental data pertaining to specific structures. However, the most reliable method to obtain structural information construct models is based on the known structural information from homologous proteins. This process is referred to as homology modeling. Advances in homology modeling will provide a number of critical applications in drug design/discovery and is believed to offer a variety of novel research applications with respect to the discovery of novel phytoconstituents. This chapter aims to provide a comprehensive introduction and guide to commonly applied homology modeling tools.

Motivation Protein fold recognition has attracted increasing attention because it is critical for studies of the 3D structures of proteins and drug design. Researchers have been extensively studying this important task, and several features with high discriminative power have been proposed. However, the development of methods that efficiently combine these features to improve the predictive performance remains a challenging problem. Results In this study, we proposed two algorithms: MV-fold and MT-fold. MV-fold is a new computational predictor based on the multi-view learning model for fold recognition. Different features of proteins were treated as different views of proteins, including the evolutionary information, secondary structure information and physicochemical properties. These different views constituted the latent space. The ε-dragging technique was employed to enlarge the margins between different protein folds, improving the predictive performance of MV-fold. Then, MV-fold was combined with two template-based methods: HHblits and HMMER. The ensemble method is called MT-fold incorporating the advantages of both discriminative methods and template-based methods. Experimental results on five widely used benchmark datasets (DD, RDD, EDD, TG and LE) showed that the proposed methods outperformed some state-of-the-art methods in this field, indicating that MV-fold and MT-fold are useful computational tools for protein fold recognition and protein homology detection and would be efficient tools for protein sequence analysis. Finally, we constructed an update and rigorous benchmark dataset based on SCOPe (version 2.07) to fairly evaluate the performance of the proposed method, and our method achieved stable performance on this new dataset. This new benchmark dataset will become a widely used benchmark dataset to fairly evaluate the performance of different methods for fold recognition. Supplementary information Supplementary data are available at Bioinformatics online.

PDZ domains represent one of the most common protein homology regions playing key roles in several diseases. Point mutations (PM) in amino acid primary sequence of PDZ domains can alter domain functions by affecting for example, downstream phosphorylation, a pivotal process in biology. Our goal in this present study was to introduce a novel approach to investigate how point mutations within the Class 1, Class 2 and Class 1–2 PDZ domains could affect the changes in binding with their partner ligands and hence affect their classification. We focused on features in PDZ domains of various species including human, rat and mouse. However, our work represents a generic computational framework that could be used to analyze PM in any given PDZ sequence. We have adopted two different approaches to investigate the impact of PM. In the first approach, we have developed a statistical model using bigram frequencies of amino acids and employed six different similarity measures to contrast the bigram frequency distribution of a wild type sequence relevant to its point mutants. In the next approach, we developed a statistical method that incorporates the impact of bigram frequency history associated with each mutational site that we call history weighted conditional change in probabilities. In this PM study, we observed that the history weighted method performs best when compared to all other methods studied in terms of picking up sites in PDZ domain where a PM could flip the class. We anticipate that this method will present a step forward towards computational techniques unveiling PDZ domain point mutants that largely affect the protein-ligand binding, specificity and affinity. We hope that this and future studies could aid therapy in which PDZ mutations have been implicated as the main disease drivers such as the Usher syndrome.

A challenging Pattern Recognition problem in Bioinformatics concerns the detection of a functional relation between two proteins even when they show very low sequence similarity – this is the so-called Protein Remote Homology Detection (PRHD) problem. In this paper we propose a novel approach to PRHD, which casts the problem into a Multiple Instance Learning (MIL) framework, which seems very suitable for this context. Experiments on a standard benchmark show very competitive performances, also in comparison with alternative discriminative methods.

Atomic models of proteins built by homology modeling or from low-resolution experimental data may contain considerable local errors. The refinement success of molecular dynamics simulations is usually limited by both force field accuracy and by the substantial width of the conformational distribution at physiological temperatures. We propose a method to overcome both these problems by coupling homologous replicas during a molecular dynamics simulation, which narrows the conformational distribution, and smoothens and even improves the energy landscape by adding evolutionary information.

Advent of Next Generation Sequencing has led to possibilities of de novo transcriptome assembly of organisms without availability of complete genome sequence. Among various sequencing platforms available, Illumina is the most widely used platform based on data quality, quantity and cost. Various de novo transcriptome assemblers are also available today for construction of de novo transcriptome.In this study, we aimed at obtaining an ameliorated de novo transcriptome assembly with sequence reads obtained from Illumina platform and assembled using Trinity Assembler. We found that, primary transcriptome assembly obtained as a result of Trinity can be ameliorated on the basis of transcript length, coverage, and depth and protein homology. Our approach to ameliorate is reproducible and could enhance the sensitivity and specificity of the assembled transcriptome which could be critical for validation of the assembled transcripts and for planning various downstream biological assays.

In this paper, a new method for remote protein homology detection is presented. Most discriminative methods concatenate the values extracted from physicochemical properties to build a model that separates homolog and non-homolog examples. Each discriminative method uses a specific strategy to represent the information extracted from the protein sequence and a different number of indices. After the vector representation is achieved, support vector machines (SVM) are usually used. Most classification techniques are not suitable in remote homology detection because they do not address high dimensional datasets. In this paper, we propose a method that reduces the high dimensionality of the vector representation using models that are defined at the 3D level. Next, the models are mapped from the protein primary sequence. The new method, called remote-C3D, is presented and tested on the SCOP 1.53 and SCOP 1.55 datasets. The remote-C3D method achieves a higher accuracy than the composition-based methods and a comparable performance with profile-based methods. Predicted contact maps can be used to detect remote homology.The dimensionality of the protein representation can be reduced.A new discriminative strategy to detect remote homology is proposed.We model every protein family using diverse classification techniques.

Motivation: The functional impact of small molecules is increasingly being assessed in different eukaryotic species through large-scale phenotypic screening initiatives. Identifying the targets of these molecules is crucial to mechanistically understand their function and uncover new therapeutically relevant modes of action. However, despite extensive work carried out in model organisms and human, it is still unclear to what extent one can use information obtained in one species to make predictions in other species. Results: Here, for the first time, we explore and validate at a large scale the use of protein homology relationships to predict the targets of small molecules across different species. Our results show that exploiting target homology can significantly improve the predictions, especially for molecules experimentally tested in other species. Interestingly, when considering separately orthology and paralogy relationships, we observe that mapping small molecule interactions among orthologs improves prediction accuracy, while including paralogs does not improve and even sometimes worsens the prediction accuracy. Overall, our results provide a novel approach to integrate chemical screening results across multiple species and highlight the promises and remaining challenges of using protein homology for small molecule target identification. Availability and implementation: Homology-based predictions can be tested on our website http://www.swisstargetprediction.ch. Contact: david.gfeller@unil.ch or vincent.zoete@isb-sib.ch. Supplementary information: Supplementary data are available at Bioinformatics online.

Motivation: Homology models of proteins are of great interest for planning and analysing biological experiments when no experimental three-dimensional structures are available. Building homology models requires specialized programs and up-to-date sequence and structural databases. Integrating all required tools, programs and databases into a single web-based workspace facilitates access to homology modelling from a computer with web connection without the need of downloading and installing large program packages and databases. Results: SWISS-MODEL workspace is a web-based integrated service dedicated to protein structure homology modelling. It assists and guides the user in building protein homology models at different levels of complexity. A personal working environment is provided for each user where several modelling projects can be carried out in parallel. Protein sequence and structure databases necessary for modelling are accessible from the workspace and are updated in regular intervals. Tools for template selection, model building and structure quality evaluation can be invoked from within the workspace. Workflow and usage of the workspace are illustrated by modelling human Cyclin A1 and human Transmembrane Protease 3. Availability: The SWISS-MODEL workspace can be accessed freely at Contact: Torsten.Schwede@unibas.ch Supplementary information: Supplementary data are available at Bioinformatics online.

A new method for remote protein homology detection, called CDA (Characteristic Distribution Analysis), is presented. The CDA method uses the distributions of physicochemical properties of amino acids for each protein. Given the training sequences of a SCOP (Structural Classification Of Proteins) family, a characteristic distribution is achieved by averaging the values of the distributions of its proteins. The hypothesis in this research is that each protein family F has a characteristic distribution that separates its sequences from the rest of the proteins in a dataset. Since there are multiple properties, close to 554 in the AAindex, a set of 72 physicochemical properties was selected to create different characteristic distributions of the same family. Each characteristic distribution is used as a classifier. Finally, a Naive Bayes classifier is trained to combine the information of the individual classifiers and obtain a better decision. We found that each family has a set of physicochemical properties that allow the discrimination of their sequences better. CDA achieves a True Positive (TP) rate of 0,793, a False Positive (FP) rate of 0,005, and a Receiver Operating Characteristic (ROC) area of 0,918. The CDA method outperforms some of the current strategies such as SVM-PCD and SVM-RQA. En este artículo se presenta un nuevo método para la detección de homólogos remotos en proteínas llamado CDA (Análisis de Distribución Característica). El método utiliza distribuciones de las propiedades fisicoquímicas de los aminoácidos para cada proteína. Dada una familia SCOP se calcula su correspondiente distribución característica promediando los valores de las distribuciones para las proteínas que la componen. La hipótesis en está investigación es que cada familia F tiene una distribución característica que permite diferenciar las secuencias del resto de las proteínas en el conjunto de datos. Debido a que existen muchas propiedades, alrededor de 554 en el AAindex, se seleccionó un conjunto de 72 índices para crear las distribuciones. Cada distribución característica se usa como un clasificador de familias SCOP. Por último, se utiliza una clasificador Bayesiano para combinar la información de los clasificadores individuales creados a partir de las distribuciones para llegar a una mejor decisión. Encontramos que cada familia tiene un conjunto de propiedades físicoquímicas que permiten una mejor discriminación de sus secuencias. El método CDA alcanza una tasa de Aciertos Positivos (TP) de 0,793, una tasa de Falsos Positivos (FP) de 0,005 y un puntaje ROC de 0,918. El método propuesto mejora la exactitud de algunas de las estrategias existentes tales como SVM-PCD y SVM-RQA. Neste artigo apresenta-se um novo método para a detecção de homólogos remotos em proteínas chamado CDA (Análises de Distribuição Característica). O método utiliza distribuições das propriedades fisicoquímicas dos aminoácidos. Dada uma família SCOP calcula-se sua correspondente distribuição característica promediando os valores das distribuições para as proteínas que a compõem. A hipótese nesta investigação é que cada família F tem uma distribuição característica que permite diferenciar as sequências em F do resto de proteínas. Ao existir muitas propriedades, ao redor de 554 no AAindex, selecionou-se um conjunto de 72 índices para criar as distribuições. Cada distribuição característica usa-se como um classificador de famílias SCOP. Por último, utiliza-se um classificador Bayesiano para combinar a informação dos classificadores individuais criados a partir das distribuições. O método CDA atinge uma taxa de acertos positivos de 0,793, uma taxa de falsos positivos de 0,005 e uma pontuação ROC de 0,918. O método proposto melhora a exatidão de algumas das estratégias existentes tais como SVM-PCD e SVM-RQA.

A significant challenge and potential high-value application of computer-aided drug design is the accurate prediction of protein–ligand binding affinities. Free energy perturbation (FEP) using molecular dynamics (MD) sampling is among the most suitable approaches to achieve accurate binding free energy predictions, due to the rigorous statistical framework of the methodology, correct representation of the energetics, and thorough treatment of the important degrees of freedom in the system (including explicit waters). Recent advances in sampling methods and force fields coupled with vast increases in computational resources have made FEP a viable technology to drive hit-to-lead and lead optimization, allowing for more efficient cycles of medicinal chemistry and the possibility to explore much larger chemical spaces. However, previous FEP applications have focused on systems with high-resolution crystal structures of the target as starting points—something that is not always available in drug discovery projects. As such, the ability to apply FEP on homology models would greatly expand the domain of applicability of FEP in drug discovery. In this work we apply a particular implementation of FEP, called FEP+, on congeneric ligand series binding to four diverse targets: a kinase (Tyk2), an epigenetic bromodomain (BRD4), a transmembrane GPCR (A2A), and a protein–protein interaction interface (BCL-2 family protein MCL-1). We apply FEP+ using both crystal structures and homology models as starting points and find that the performance using homology models is generally on a par with the results when using crystal structures. The robustness of the calculations to structural variations in the input models can likely be attributed to the conformational sampling in the molecular dynamics simulations, which allows the modeled receptor to adapt to the “real” conformation for each ligand in the series. This work exemplifies the advantages of using all-atom simulation methods with full system flexibility and offers promise for the general application of FEP to homology models, although additional validation studies should be performed to further understand the limitations of the method and the scenarios where FEP will work best.

MotivationShotgun metagenomics is a powerful, high-resolution technique enabling the study of microbial communities in situ. However, species-level resolution is only achieved after a process of “binning” where contigs predicted to originate from the same genome are clustered. Such culture-independent sequencing frequently unearths novel microbes, and so various methods have been devised for reference-free binning. Existing methods, however, suffer from: (1) reliance on human pattern recognition, which is inherently unscalable; (2) requirement for multiple co-assembled metagenomes, which degrades assembly quality due to strain variance; and (3) assumption of prior host genome removal not feasible for non-model hosts. We therefore devised a fully-automated pipeline, termed “Autometa,” to address these issues. Results: Autometa implements a method for taxonomic partitioning of contigs based on predicted protein homology, and this was shown to vastly improve binning in host-associated and complex metagenomes. Autometa’s method of automated clustering, based on Barnes-Hut Stochastic Neighbor Embedding (BH-tSNE) and DBSCAN, was shown to be highly scalable, outperforming other binning pipelines in complex simulated datasets.Availability and implementationAutometa is freely available at https://bitbucket.org/jasonckwan/autometa and as a docker image at https://hub.docker.com/r/jasonkwan/autometa under the GNU Affero General Public License 3 (AGPL 3).Contactjason.kwan@wisc.eduSupplementary informationSupplementary data are available attached to this article at https://biorxiv.org

Our goal in this present study is to introduce new wavelet based methods for differentiating and classifying Class I and Class II PDZ domains and compare the resulting signals. PDZ domains represent one of the most common protein homology regions playing key roles in several diseases. To perform the classification, we developed two methods. The first of our methods was comparable to the standard wavelet approaches while the second one surpasses it in recognition accuracy. Our models exhibited interesting results, and we anticipate that it can be used as a computational technique to screen out the misfit candidates and to reduce the search space, while achieving high classification and accuracy.

The amount and quality of available data on different organisms varies greatly. While model organisms benefit from extensive experimental studies, there is often a lack of detailed experimental data for more specific organisms. Additionally, even among model organisms there are noticeable differences in the amount and type of data available, due to the different suitability of experiments in different organisms. The combination of interactomes for closely related species, represents a viable tool to increase the amount of protein-protein interaction data for a given organism. The Human-Mouse case of study is particularly relevant, as many experiments cannot be carried out on humans. This paper describes a general method to construct a combined interactome from different organisms. The construction is achieved through the integration of data from different sources and formats, including gene-protein relations, protein homology classes and protein-protein interactions. We show that the Human-Mouse combined interactome increase the mouse gene coverage by over 150% and the interaction coverage by over 430%. We also provide a novel mathematical formalisation for the interactome combination.

Rapid advances in genomic sequencing technology have resulted in a data deluge in biology and bioinformatics. This increase in data volumes has introduced computational challenges for frequently performed sequence analytics routines such as DNA and protein homology searches, these must also preferably be done in real-time. In this paper, we propose a scalable and similarity-aware distributed storage framework, Mendel, that enables retrieval of biologically significant DNA and protein alignments against a voluminous genomic sequence database. Mendel fragments the sequence data and generates an inverted-index, which is then dispersed over a distributed collection of machines using a locality aware distributed hash table. A novel distributed nearest neighbor search algorithm identifies sequence segments with high similarity and extending them to find an alignment. This paper includes an empirical evaluation of the performance, sensitivity, and scalability of the proposed system versus the National Center for Biotechnology Information's non-redundant protein dataset. Mendel demonstrates higher sensitivity and faster query evaluations when compared to other modern frameworks.

Background BLOSUM matrices belong to the most commonly used substitution matrix series for protein homology search and sequence alignments since their publication in 1992. In 2008, Styczynski et al. discovered miscalculations in the clustering step of the matrix computation. Still, the RBLOSUM64 matrix based on the corrected BLOSUM code was reported to perform worse at a statistically significant level than the BLOSUM62. Here, we present a further correction of the (R)BLOSUM code and provide a thorough performance analysis of BLOSUM-, RBLOSUM- and the newly derived CorBLOSUM-type matrices. Thereby, we assess homology search performance of these matrix-types derived from three different BLOCKS databases on all versions of the ASTRAL20, ASTRAL40 and ASTRAL70 subsets resulting in 51 different benchmarks in total. Our analysis is focused on two of the most popular BLOSUM matrices — BLOSUM50 and BLOSUM62. Results Our study shows that fixing small errors in the BLOSUM code results in substantially different substitution matrices with a beneficial influence on homology search performance when compared to the original matrices. The CorBLOSUM matrices introduced here performed at least as good as their BLOSUM counterparts in ∼75 % of all test cases. On up-to-date ASTRAL databases BLOSUM matrices were even outperformed by CorBLOSUM matrices in more than 86 % of the times. In contrast to the study by Styczynski et al., the tested RBLOSUM matrices also outperformed the corresponding BLOSUM matrices in most of the cases. Comparing the CorBLOSUM with the RBLOSUM matrices revealed no general performance advantages for either on older ASTRAL releases. On up-to-date ASTRAL databases however CorBLOSUM matrices performed better than their RBLOSUM counterparts in ∼74 % of the test cases. Conclusions Our results imply that CorBLOSUM type matrices outperform the BLOSUM matrices on a statistically significant level in most of the cases, especially on up-to-date databases such as ASTRAL ≥2.01. Additionally, CorBLOSUM matrices are closer to those originally intended by Henikoff and Henikoff on a conceptual level. Hence, we encourage the usage of CorBLOSUM over (R)BLOSUM matrices for the task of homology search. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-1060-3) contains supplementary material, which is available to authorized users.

Candidatus Liberibacter asiaticus (Ca. L. asiaticus) is a Gram-negative bacterium and the pathogen of Citrus Greening disease (Huanglongbing, HLB). As a parasitic bacterium, Ca. L. asiaticus harbors ABC transporters that play important roles in exchanging chemical compounds between Ca. L. asiaticus and its host. Here, we analyzed all the ABC transporter-related proteins in Ca. L. asiaticus. We identified 14 ABC transporter systems and predicted their structures and substrate specificities. In-depth sequence and structure analysis including multiple sequence alignment, phylogenetic tree reconstruction, and structure comparison further support their function predictions. Our study shows that this bacterium could use these ABC transporters to import metabolites (amino acids and phosphates) and enzyme cofactors (choline, thiamine, iron, manganese, and zinc), resist to organic solvent, heavy metal, and lipid-like drugs, maintain the composition of the outer membrane (OM), and secrete virulence factors. Although the features of most ABC systems could be deduced from the abundant experimental data on their orthologs, we reported several novel observations within ABC system proteins. Moreover, we identified seven nontransport ABC systems that are likely involved in virulence gene expression regulation, transposon excision regulation, and DNA repair. Our analysis reveals several candidates for further studies to understand and control the disease, including the type I virulence factor secretion system and its substrate that are likely related to Ca. L. asiaticus pathogenicity and the ABC transporter systems responsible for bacterial OM biosynthesis that are good drug targets.

The BioShell package has recently been extended with a web server for protein homology detection based on profile-to-profile alignment (known as 1D threading). Its aim is to assign structural templates to each domain of the query. The server uses sequence profiles that describe observed sequence variability and secondary structure profiles providing expected probability for a certain secondary structure type at a given position in a protein. Three independent predictors are used to increase the rate of successful predictions. Careful evaluation shows that there is nearly 80% chance that the query sequence belongs to the same SCOP family as the top scoring template. The Bioshell Threader server is freely available at: http://www.bioshell.pl/threader/.

Angiosperm stigma supports compatible pollen germination and tube growth, resulting in fertilization and seed production. Stigmas are mainly divided into two types, dry and wet, according to the absence or presence of exudates on their surfaces. Here, we used 2DE and MS to identify proteins specifically and preferentially expressed in the stigmas of maize (Zea Mays, dry stigma) and tobacco (Nicotiana tabacum, wet stigma), as well as proteins rinsed from the surface of the tobacco stigma. We found that the specifically and preferentially expressed proteins in maize and tobacco stigmas share similar distributions in functional categories. However, these proteins showed important difference between dry and wet stigmas in a few aspects, such as protein homology in "signal transduction" and "lipid metabolism," relative expression levels of proteins containing signal peptides and proteins in "defense and stress response." These different features might be related to the specific structures and functions of dry and wet stigmas. The possible roles of some stigma-expressed proteins were discussed. Our results provide important information on functions of proteins in dry and wet stigmas and reveal aspects of conservation and divergence between dry and wet stigmas at the proteomic level.

Molecular approaches in ecotoxicology have greatly enhanced mechanistic understanding of the impact of aquatic pollutants in organisms. These methods+- have included high throughput Omics technologies, including quantitative proteomics methods such as 2D differential in-gel electrophoresis (DIGE) and isobaric tagging for relative and absolute quantitation (iTRAQ). These methods are becoming more widely used in ecotoxicology studies to identify and characterize protein bioindicators of adverse effects. In teleost fish, iTRAQ has been used successfully in different fish species (e.g. fathead minnow, goldfish, largemouth bass) and tissues (e.g. hypothalamus and liver) to quantify relative protein abundance. Of interest for ecotoxicology is that many proteins commonly utilized as bioindicators of toxicity or stress are quantifiable using iTRAQ on a larger scale, providing a global baseline of biological effect from which to assess additional changes in the proteome. This review highlights the successes to date for high throughput quantitative proteomics using DIGE and iTRAQ in aquatic toxicology. Current challenges for the iTRAQ method for biomarker discovery in fish are the high cost and the lack of complete annotated genomes for teleosts. However, the use of protein homology from teleost fishes in protein databases and the introduction of hybrid LTQ-FT (Linear ion trap –Fourier transform) mass spectrometers with high resolution, increased sensitivity, and high mass accuracy are able to improve significantly the protein identification rates. Despite these challenges, initial studies utilizing iTRAQ for ecotoxicoproteomics have exceeded expectations and it is anticipated that the use of non-gel based quantitative proteomics will increase for protein biomarker discovery and for characterization of chemical mode of action.

Protein homology detection is an important issue in comparative genomics. Because of the exponential growth of sequence databases, fast and efficient homology detection tools are urgently needed. Currently, for homology detection, sequence comparison methods using local alignment such as BLAST are generally used as they give a reasonable measure for sequence similarity. However, these methods have drawbacks in offering overall sequence similarity, especially in dealing with eukaryotic genomes that often contain many insertions and duplications on sequences. Also these methods do not provide the explicit models for speciation, thus it is difficult to interpret their similarity measure into homology detection. Here, we present a novel method based on Word Conservation Score (WCS) to address the current limitations of homology detection. Instead of counting each amino acid, we adopted the concept of `Word` to compare sequences. WCS measures overall sequence similarity by comparing word contents, which is much faster than BLAST comparisons. Furthermore, evolutionary distance between homologous sequences could be measured by WCS. Therefore, we expect that sequence comparison with WCS is useful for the multiple-species-comparisons of large genomes. In the performance comparisons on protein structural classifications, our method showed a considerable improvement over BLAST. Our method found bigger micro-syntenic blocks which consist of orthologs with conserved gene order. By testing on various datasets, we showed that WCS gives faster and better overall similarity measure compared to BLAST.

Remote protein homology detection and fold recognition refer to detection of structural homology in proteins where there are small or no similarities in the sequence. To detect protein structural classes from protein primary sequence information, homology-based methods have been developed, which can be divided to three types: discriminative classifiers, generative models for protein families and pairwise sequence comparisons. Support Vector Machines (SVM) and Neural Networks (NN) are two popular discriminative methods. Recent studies have shown that SVM has fast speed during training, more accurate and efficient compared to NN. We present a comprehensive method based on two-layer classifiers. The 1st layer is used to detect up to superfamily and family in SCOP hierarchy using optimized binary SVM classification rules. It used the kernel function known as the Bio-kernel, which incorporates the biological information in the classification process. The 2nd layer uses discriminative SVM algorithm with string kernel that will detect up to protein fold level in SCOP hierarchy. The results obtained were evaluated using mean ROC and mean MRFP and the significance of the result produced with pairwise t-test was tested. Experimental results show that our approaches significantly improve the performance of remote protein homology detection and fold recognition for all three different version SCOP datasets (1.53, 1.67 and 1.73). We achieved 4.19% improvements in term of mean ROC in SCOP 1.53, 4.75% in SCOP 1.67 and 4.03% in SCOP 1.73 datasets when compared to the result produced by well-known methods. The combination of first layer and second layer of BioSVM-2L performs well in remote homology detection and fold recognition even in three different versions of datasets.

We study the explosive character of the percolation transition in a real-world network. We show that the emergence of a spanning cluster in the Human Protein Homology Network (H-PHN) exhibits similar features to an Achlioptas-type process and is markedly different from regular random percolation. The underlying mechanism of this transition can be described by slow-growing clusters that remain isolated until the later stages of the process, when the addition of a small number of links leads to the rapid interconnection of these modules into a giant cluster. Our results indicate that the evolutionary-based process that shapes the topology of the H-PHN through duplication-divergence events may occur in sudden steps, similarly to what is seen in first-order phase transitions., 13 pages, 6 figures

Thyroid Hormone Receptor β (THRB) defects, typically transmitted as autosomal dominant traits, cause Resistance to Thyroid Hormone (RTH). We analyzed the THRB gene in thirteen South American patients with clinical evidence RTH from eleven unrelated families. Sequence analysis revealed seven novel missense mutations. Four novel mutations were identified in exon 9. The first, a c.991A>G transition which originates a substitution of asparagine by aspartic acid (p.N331D). The second nucleotide alteration consists of a guanine to cytosine transversion at position 1003 (c.1003G>C) and results in substitution of the alanine at codon 335 by proline (p.A335P). The third mutation, a c.1022T>C transition produces a change of leucine by proline (p.L341P). The fourth mutation detected in exon 9 was a c.1036C>T transition which replaces the leucine at codon 346 by phenylalanine (p.L346F). The sequencing of the exon 10 detected three novel missense mutations. The first, a c.1293A>G transition changing isoleucine 431 for methionine (p.I431M). The second, the cytosine at position 1339 was replaced by adenine (c.1339C>A) resulting in the replacement of proline by threonine (p.P447T). The third mutation detected in exon 10 was a c.1358C>T transition resulting in the substitution of proline at codon 453 by leucine (p.P453L). Finally, sequencing analysis of the THRB gene revealed three substitutions previously described (p.A268G, p.P453T and p.F459C). The p.P453T was found in two patients. In conclusion, we report thirteen patients with RTH caused by heterozygous mutations of the THRB gene. Seven of the identified mutations correspond to novel substitutions. Fil: Rivolta, Carina Marcela. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Departamento de Microbiología, Inmunología y Biotecnología. Cátedra de Genética y Biología Molecular; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Inmunología, Genética y Metabolismo. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Inmunología, Genética y Metabolismo; Argentina. Universidad de Salamanca; España Fil: Olcese, María Cecilia. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Departamento de Microbiología, Inmunología y Biotecnología. Cátedra de Genética y Biología Molecular; Argentina Fil: Belforte, Fiorella Sabrina. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Departamento de Microbiología, Inmunología y Biotecnología. Cátedra de Genética y Biología Molecular; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Chiesa, Ana Elena. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Centro de Investigaciones Endocrinológicas "Dr. César Bergada". Gobierno de la Ciudad de Buenos Aires. Centro de Investigaciones Endocrinológicas "Dr. César Bergada". Fundación de Endocrinología Infantil. Centro de Investigaciones Endocrinológicas "Dr. César Bergada"; Argentina Fil: Gruñeiro Papendieck, Laura. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Centro de Investigaciones Endocrinológicas "Dr. César Bergada". Gobierno de la Ciudad de Buenos Aires. Centro de Investigaciones Endocrinológicas "Dr. César Bergada". Fundación de Endocrinología Infantil. Centro de Investigaciones Endocrinológicas "Dr. César Bergada"; Argentina Fil: Iorcansky, Sonia. Gobierno de la Ciudad de Buenos Aires. Hospital de Pediatría "Juan P. Garrahan"; Argentina Fil: Herzovich, Viviana. Gobierno de la Ciudad de Buenos Aires. Hospital de Pediatría "Juan P. Garrahan"; Argentina Fil: Cassorla, Fernando. Universidad de Santiago de Chile. Hospital Clinico San Borja Arriaran. Instituto de Investigaciones Materno Infantil; Chile Fil: Gauna, Alicia Teresa. Gobierno de la Ciudad de Buenos Aires. Hospital General de Agudos "Ramos Mejía"; Argentina Fil: Gonzalez Sarmiento, Rogelio. Universidad de Salamanca; España Fil: Targovnik, Hector Manuel. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Departamento de Microbiología, Inmunología y Biotecnología. Cátedra de Genética y Biología Molecular; Argentina. Universidad de Salamanca; España. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Inmunología, Genética y Metabolismo. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Inmunología, Genética y Metabolismo; Argentina

Ebolavirus and Marburgvirus (belonging to the Filoviridae family) emerged four decades ago and cause epidemics of haemorrhagic fever with high case-fatality rates. The genome of filoviruses encodes seven proteins. No significant homology is observed between filovirus proteins and any known macromolecule. Moreover, Marburgvirus and Ebolavirus show significant differences in protein homology. The natural maintenance cycle of filoviruses is unknown, the natural reservoir, the mode of transmission, the epidemic disease generation, and temporal dynamics are unclear. Lastly, Ebolavirus and Marburgvirus are considered as potential biological weapons. Vaccine appears the unique therapeutic frontier. Here, molecular and clinical aspects of filoviral haemorrhagic fevers are summarized. (c) 2007 Published by Elsevier Ltd.

Remote homology detection refers to the detection of structure homology in evolutionarily related proteins with low sequence similarity. Supervised learning algorithms such as support vector machine (SVM) are currently the most accurate methods. In most of these SVM-based methods, efforts have been dedicated to developing new kernels to better use the pairwise alignment scores or sequence profiles. Moreover, amino acids' physicochemical properties are not generally used in the feature representation of protein sequences. In this article, we present a remote homology detection method that incorporates two novel features: (1) a protein's primary sequence is represented using amino acid's physicochemical properties and (2) the similarity between two proteins is measured using recurrence quantification analysis (RQA). An optimization scheme was developed to select different amino acid indices (up to 10 for a protein family) that are best to characterize the given protein family. The selected amino acid indices may enable us to draw better biological explanation of the protein family classification problem than using other alignment-based methods. An SVM-based classifier will then work on the space described by the RQA metrics. The classification scheme is named as SVM-RQA. Experiments at the superfamily level of the SCOP1.53 dataset show that, without using alignment or sequence profile information, the features generated from amino acid indices are able to produce results that are comparable to those obtained by the published state-of-the-art SVM kernels. In the future, better prediction accuracies can be expected by combining the alignment-based features with our amino acids property-based features. Supplementary information including the raw dataset, the best-performing amino acid indices for each protein family and the computed RQA metrics for all protein sequences can be downloaded from http://ym151113.ym.edu.tw/svm-rqa.

There are abundance of transcripts that code for no particular protein and that remain functionally uncharacterized. Some of these transcripts may have novel functions while others might be junk transcripts. Unfortunately, the experimental validation of such transcripts to find functional non-coding RNA candidates is very costly. Therefore, our primary interest is to computationally mine candidate functional transcripts from a pool of uncharacterized transcripts. We introduce fRNAdb: a novel database service that hosts a large collection of non-coding transcripts including annotated/non-annotated sequences from the H-inv database, NONCODE and RNAdb. A set of computational analyses have been performed on the included sequences. These analyses include RNA secondary structure motif discovery, EST support evaluation, cis-regulatory element search, protein homology search, etc. fRNAdb provides an efficient interface to help users filter out particular transcripts under their own criteria to sort out functional RNA candidates. fRNAdb is available at http://www.ncrna.org/

Background Uncovering the hidden organizational characteristics and regularities among biological sequences is the key issue for detailed understanding of an underlying biological phenomenon. Thus pattern recognition from nucleic acid sequences is an important affair for protein function prediction. As proteins from the same family exhibit similar characteristics, homology based approaches predict protein functions via protein classification. But conventional classification approaches mostly rely on the global features by considering only strong protein similarity matches. This leads to significant loss of prediction accuracy. Methods Here we construct the Protein-Protein Similarity (PPS) network, which captures the subtle properties of protein families. The proposed method considers the local as well as the global features, by examining the interactions among 'weakly interacting proteins' in the PPS network and by using hierarchical graph analysis via the graph pyramid. Different underlying properties of the protein families are uncovered by operating the proposed graph based features at various pyramid levels. Results Experimental results on benchmark data sets show that the proposed hierarchical voting algorithm using graph pyramid helps to improve computational efficiency as well the protein classification accuracy. Quantitatively, among 14,086 test sequences, on an average the proposed method misclassified only 21.1 sequences whereas baseline BLAST score based global feature matching method misclassified 362.9 sequences. With each correctly classified test sequence, the fast incremental learning ability of the proposed method further enhances the training model. Thus it has achieved more than 96% protein classification accuracy using only 20% per class training data.

Background The Avrk1 and Avra10 avirulence (AVR) genes encode effectors that increase the pathogenicity of the fungus Blumeria graminis f.sp. hordei (Bgh), the powdery mildew pathogen, in susceptible barley plants. In resistant barley, MLK1 and MLA10 resistance proteins recognize the presence of AVRK1 and AVRA10, eliciting the hypersensitive response typical of gene for gene interactions. Avrk1 and Avra10 have more than 1350 homologues in Bgh genome, forming the EKA (Effectors homologous to Avrk1 and Avra10) gene family. Results We tested the hypothesis that the EKA family originated from degenerate copies of Class I LINE retrotransposons by analysing the EKA family in the genome of Bgh isolate DH14 with bioinformatic tools specially developed for the analysis of Transposable Elements (TE) in genomes. The Class I LINE retrotransposon copies homologous to Avrk1 and Avra10 represent 6.5 % of the Bgh annotated genome and, among them, we identified 293 AVR/effector candidate genes. We also experimentally identified peptides that indicated the translation of several predicted proteins from EKA family members, which had higher relative abundance in haustoria than in hyphae. Conclusions Our analyses indicate that Avrk1 and Avra10 have evolved from part of the ORF1 gene of Class I LINE retrotransposons. The co-option of Avra10 and Avrk1 as effectors from truncated copies of retrotransposons explains the huge number of homologues in Bgh genome that could act as dynamic reservoirs from which new effector genes may evolve. These data provide further evidence for recruitment of retrotransposons in the evolution of new biological functions. Electronic supplementary material The online version of this article (doi:10.1186/s12864-015-2185-x) contains supplementary material, which is available to authorized users.

In this paper, we improve the homology search performance by the combination of the predicted protein secondary structures and protein sequences. Previous research suggested that the straightforward combination of predicted secondary structures did not improve the homology search performance, mostly because of the errors in the structure prediction. We solved this problem by taking into account the confidence scores output by the prediction programs.

This research work introduces a simple method based on representing protein sequence by fix dimensions of the length three. We present hidden Markov model combining scores method. Three scoring algorithms are combined to represent protein sequence of amino acids for better remote homology detection. We tested the method on the SCOP version 1.37 dataset. The results show that, with such a simple representation, we are able to achieve superior performance to previously presented protein homology detection methods while achieving better computational efficiency.

The article presents the application of a new machine-learning algorithm for the prediction of secondary structures of proteins. The logical analysis of data (LAD) algorithm was applied to recognize which amino acids properties could be analyzed to deliver additional information, independent from protein homology, useful in determining the secondary structure of a protein. The study showed that to get better results, LAD should be used as a first stage of analysis in combination with another method that is able to take into account a more detailed understanding of the physical chemistry of proteins and amino acids.

We present a framework for improving local protein alignment algorithms. Specifically, we discuss how to extend local protein aligners to use a collection of vector seeds or ungapped alignment seeds to reduce noise hits. We model picking a set of seed models as an integer programming problem and give algorithms to choose such a set of seeds. While the problem is NP-hard, and Quasi-NP-hard to approximate to within a logarithmic factor, it can be solved easily in practice. A good set of seeds we have chosen allows four to five times fewer false positive hits, while preserving essentially identical sensitivity as BLASTP.

This paper describes our solution for the protein homology prediction task in KDD Cup 2004 competition. This task is modeled as a supervised learning problem with multiple performance metrics. Several key characteristics make the problem both novel and challenging, including the concept of data blocks and the presence of large-scale and imbalanced training data. These features make a naive application of the traditional classification algorithms infeasible. Our approach focuses on making full use of the abundant information within the blocks, and developing a new technique for reducing and balancing training data to make the support vector machine applicable to this kind of large-scale and imbalanced learning tasks.

This paper summarizes and analyzes the results of the 2004 KDD-Cup. The competition consisted of two tasks from the areas of particle physics and protein homology detection. It focused on the problem of optimizing supervised learning to different performance measures (accuracy, cross-entropy, ROC area, SLAC-Q, squared error, average precision, top 1, and rank of last). A total of 102 groups participated in the competition, 6 of which received awards or honorable mentions. Their approaches are described in other papers in this issue of SIGKDD Explorations. In this paper we do not analyze any particular approach, but give insight into the performance of the field of competitors as a whole. We study what fraction of the participants found good solutions, how well participants were able to optimize to different performance measures, how homogeneous their submitted predictions are, and if the best submissions represent the maximal performances that could reasonably be achieved. We are keeping the KDD-Cup 2004 WWW site open and have added an automatic scoring system for new submissions in order to encourage further research in this area.

In this paper we describe the winning model for the performance measure "lowest ranked homologous sequence" (RKL). This was a subtask of the Protein Homology Prediction task of the KDD Cup 2004. The goal was to predict protein homology for different performance metrics. The given data was organized in blocks, each of which corresponds to a specific native sequence. The two metrics average precision (APR) and RKL explicitly make use of this block structure. Our solution consists of two parts. The first one is a global classification SVM not aware of the block structure. The second part is a k-NearestNeighbor scheme for block similarity, used to train ranking SVMs on the fly. Furthermore, we sketch our approach to optimize the root-mean-squared-error and report some alternative solutions that turned out to be suboptimal.

Few years back, Jaakkola and Haussler published a method of combining generative and discriminative approaches for detecting protein homologies. The method was a variant of support vector machines using a new kernel function called Fisher Kernel. They begin by training a generative hidden Markov model for a protein family. Then, using the model, they derive a vector of features called Fisher scores that are assigned to the sequence and then use support vector machine in conjunction with the fisher scores for protein homologies detection. In this paper, we revisit the idea of using a discriminative approach, and in particular support vector machines for protein homologies detection. However, in place of the Fisher scoring method, we present a new Hidden Markov Model Combining Scores approach. Six scoring algorithms are combined as a way of extracting features from a protein sequence. Experiments show that our method, improves on previous methods for homologies detection of protein domains.

Motivation: Remote homology detection between protein sequences is a central problem in computational biology. Discriminative methods involving support vector machines (SVMs) are currently the most effective methods for the problem of superfamily recognition in the Structural Classification Of Proteins (SCOP) database. The performance of SVMs depends critically on the kernel function used to quantify the similarity between sequences. Results: We propose new kernels for strings adapted to biological sequences, which we call local alignment kernels. These kernels measure the similarity between two sequences by summing up scores obtained from local alignments with gaps of the sequences. When tested in combination with SVM on their ability to recognize SCOP superfamilies on a benchmark dataset, the new kernels outperform state-of-the-art methods for remote homology detection. Availability: Software and data available upon request.

High-throughput proteomics technologies, especially the yeast twohybrid system, produce large volumes of protein– protein interaction data organized in networks. The complete sequencing of many genomes raises questions about the extent to which such networks can be transferred between organisms. We attempted to answer this question using the experimentally derived Helicobacter pylori interaction map and the recently described interacting domain profile pair (IDPP) method to predict a virtual map for Escherichia coli. The extensive literature concerning E. coli was used to assess all predicted interactions and to validate the IDPP method, which clusters protein domains by sequence and connectivity similarities. The IDPP method has a much better heuristic value than methods solely based on protein homology. The IDPP method was further applied to Campylobacter jejuni to generate a virtual interaction map. An in-depth comparison of the chemotaxis pathways predicted in E. coli and C. jejuni led to the proposition of new functional assignments. Finally, the prediction of protein –protein interaction maps across organisms enabled us to validate some of the interactions on the original experimental map. q 2002 Elsevier Science Ltd. All rights reserved

Peroxidases are oxidoreductases that utilize hydrogen peroxide to catalyze oxidative reactions. A large number of peroxidases have been identified in fungal species and are being characterized at the molecular level. In this manuscript we review the current knowledge on the molecular aspects of this type of enzymes. We present an overview of the research efforts undertaken in deciphering the structural basis of the catalytic properties of fungal peroxidases and discuss molecular genetics and protein homology aspects of this enzyme class. Finally, we summarize the potential biotechnological applications of these enzymes and evaluate recent advances on their expression in heterologous systems for production purposes. © 2002 Elsevier Science B.V. All rights reserved. Molecular Sequence Numbers: GENBANK: AJ300448; Chemicals/CAS: Peroxidases, EC 1.11.1.-; Recombinant Proteins