A computational approach for detecting peptidases and their specific inhibitors at the genome level

<p>Abstract</p> <p>Background</p> <p>Peptidases are proteolytic enzymes responsible for fundamental cellular activities in all organisms. Apparently about 2–5% of the genes encode for peptidases, irrespectively of the organism source. The basic peptidase function is "protein digestion" and this can be potentially dangerous in living organisms when it is not strictly controlled by specific inhibitors. In genome annotation a basic question is to predict gene function. Here we describe a computational approach that can filter peptidases and their inhibitors out of a given proteome. Furthermore and as an added value to MEROPS, a specific database for peptidases already available in the public domain, our method can predict whether a pair of peptidase/inhibitor can interact, eventually listing all possible predicted ligands (peptidases and/or inhibitors).</p> <p>Results</p> <p>We show that by adopting a decision-tree approach the accuracy of PROSITE and HMMER in detecting separately the four major peptidase types (Serine, Aspartic, Cysteine and Metallo- Peptidase) and their inhibitors among a non redundant set of globular proteins can be improved by some percentage points with respect to that obtained with each method separately. More importantly, our method can then predict pairs of peptidases and interacting inhibitors, scoring a joint global accuracy of 99% with coverage for the positive cases (peptidase/inhibitor) close to 100% and a correlation coefficient of 0.91%. In this task the decision-tree approach outperforms the single methods.</p> <p>Conclusion</p> <p>The decision-tree can reliably classify protein sequences as peptidases or inhibitors, belonging to a certain class, and can provide a comprehensive list of possible interacting pairs of peptidase/inhibitor. This information can help the design of experiments to detect interacting peptidase/inhibitor complexes and can speed up the selection of possible interacting candidates, without searching for them separately and manually combining the obtained results. A web server specifically developed for annotating peptidases and their inhibitors (HIPPIE) is available at <url>http://gpcr.biocomp.unibo.it/cgi/predictors/hippie/pred_hippie.cgi</url></p

Predicting domain-domain interaction based on domain profiles with feature selection and support vector machines

<p>Abstract</p> <p>Background</p> <p>Protein-protein interaction (PPI) plays essential roles in cellular functions. The cost, time and other limitations associated with the current experimental methods have motivated the development of computational methods for predicting PPIs. As protein interactions generally occur via domains instead of the whole molecules, predicting domain-domain interaction (DDI) is an important step toward PPI prediction. Computational methods developed so far have utilized information from various sources at different levels, from primary sequences, to molecular structures, to evolutionary profiles.</p> <p>Results</p> <p>In this paper, we propose a computational method to predict DDI using support vector machines (SVMs), based on domains represented as interaction profile hidden Markov models (ipHMM) where interacting residues in domains are explicitly modeled according to the three dimensional structural information available at the Protein Data Bank (PDB). Features about the domains are extracted first as the Fisher scores derived from the ipHMM and then selected using singular value decomposition (SVD). Domain pairs are represented by concatenating their selected feature vectors, and classified by a support vector machine trained on these feature vectors. The method is tested by leave-one-out cross validation experiments with a set of interacting protein pairs adopted from the 3DID database. The prediction accuracy has shown significant improvement as compared to <it>InterPreTS </it>(Interaction Prediction through Tertiary Structure), an existing method for PPI prediction that also uses the sequences and complexes of known 3D structure.</p> <p>Conclusions</p> <p>We show that domain-domain interaction prediction can be significantly enhanced by exploiting information inherent in the domain profiles via feature selection based on Fisher scores, singular value decomposition and supervised learning based on support vector machines. Datasets and source code are freely available on the web at <url>http://liao.cis.udel.edu/pub/svdsvm</url>. Implemented in Matlab and supported on Linux and MS Windows.</p

Small Open Reading Frames, How to Find Them and Determine Their Function

Advances in genomics and molecular biology have revealed an abundance of small open reading frames (sORFs) across all types of transcripts. While these sORFs are often assumed to be non-functional, many have been implicated in physiological functions and a significant number of sORFs have been described in human diseases. Thus, sORFs may represent a hidden repository of functional elements that could serve as therapeutic targets. Unlike protein-coding genes, it is not necessarily the encoded peptide of an sORF that enacts its function, sometimes simply the act of translating an sORF might have a regulatory role. Indeed, the most studied sORFs are located in the 5′UTRs of coding transcripts and can have a regulatory impact on the translation of the downstream protein-coding sequence. However, sORFs have also been abundantly identified in non-coding RNAs including lncRNAs, circular RNAs and ribosomal RNAs suggesting that sORFs may be diverse in function. Of the many different experimental methods used to discover sORFs, the most commonly used are ribosome profiling and mass spectrometry. These can confirm interactions between transcripts and ribosomes and the production of a peptide, respectively. Extensions to ribosome profiling, which also capture scanning ribosomes, have further made it possible to see how sORFs impact the translation initiation of mRNAs. While high-throughput techniques have made the identification of sORFs less difficult, defining their function, if any, is typically more challenging. Together, the abundance and potential function of many of these sORFs argues for the necessity of including sORFs in gene annotations and systematically characterizing these to understand their potential functional roles. In this review, we will focus on the high-throughput methods used in the detection and characterization of sORFs and discuss techniques for validation and functional characterization.publishedVersio

Proteases in Malaria Parasites - A Phylogenomic Perspective

Malaria continues to be one of the most devastating global health problems due to the high morbidity and mortality it causes in endemic regions. The search for new antimalarial targets is of high priority because of the increasing prevalence of drug resistance in malaria parasites. Malarial proteases constitute a class of promising therapeutic targets as they play important roles in the parasite life cycle and it is possible to design and screen for specific protease inhibitors. In this mini-review, we provide a phylogenomic overview of malarial proteases. An evolutionary perspective on the origin and divergence of these proteases will provide insights into the adaptive mechanisms of parasite growth, development, infection, and pathogenesis.

Intraspecific comparative genomics of isolates of the Norway spruce pathogen (Heterobasidion parviporum) and identification of its potential virulence factors

Background: Heterobasidion parviporum is an economically most important fungal forest pathogen in northern Europe, causing root and butt rot disease of Norway spruce (Picea abies (L.) Karst.). The mechanisms underlying the pathogenesis and virulence of this species remain elusive. No reference genome to facilitate functional analysis is available for this species. Results: To better understand the virulence factor at both phenotypic and genomic level, we characterized 15 H. parviporum isolates originating from different locations across Finland for virulence, vegetative growth, sporulation and saprotrophic wood decay. Wood decay capability and latitude of fungal origins exerted interactive effects on their virulence and appeared important for H. parviporum virulence. We sequenced the most virulent isolate, the first full genome sequences of H. parviporum as a reference genome, and re-sequenced the remaining 14 H. parviporum isolates. Genome-wide alignments and intrinsic polymorphism analysis showed that these isolates exhibited overall high genomic similarity with an average of at least 96% nucleotide identity when compared to the reference, yet had remarkable intra-specific level of polymorphism with a bias for CpG to TpG mutations. Reads mapping coverage analysis enabled the classification of all predicted genes into five groups and uncovered two genomic regions exclusively present in the reference with putative contribution to its higher virulence. Genes enriched for copy number variations (deletions and duplications) and nucleotide polymorphism were involved in oxidation-reduction processes and encoding domains relevant to transcription factors. Some secreted protein coding genes based on the genome-wide selection pressure, or the presence of variants were proposed as potential virulence candidates. Conclusion: Our study reported on the first reference genome sequence for this Norway spruce pathogen (H. parviporum). Comparative genomics analysis gave insight into the overall genomic variation among this fungal species and also facilitated the identification of several secreted protein coding genes as putative virulence factors for the further functional analysis. We also analyzed and identified phenotypic traits potentially linked to its virulence.Peer reviewe

Filling the gap between biology and computer science

This editorial introduces BioData Mining, a new journal which publishes research articles related to advances in computational methods and techniques for the extraction of useful knowledge from heterogeneous biological data. We outline the aims and scope of the journal, introduce the publishing model and describe the open peer review policy, which fosters interaction within the research community

Host-parasite genomics and ecology: linking genes and transcriptomes to disease and contemporary selection

Infectious diseases in natural populations are important areas of research in ecology and evolution as they describe how parasites influence the host fitness. A host may undergo adaptive evolution against the parasite by acquiring either resistance or tolerance through developing intricate biochemical and molecular defence strategies. However, the knowledge about the genes associated with these traits remains limited. Furthermore, the strength of ongoing natural selection on transcript abundance has not been studied directly, despite the fact that gene regulation has a major role in adaptive evolution. In this thesis, I applied genomic and transcriptomic approaches to study the host parasite system of anadromous brown trout (Salmo trutta) infected with a myxozoan parasite, Tetracapsuloides bryosalmonae. In salmonids, T. bryosalmonae causes an emerging temperature-dependent disease, proliferative kidney disease (PKD). The parasite infects the kidney and spleen of juvenile fish, and at elevated temperatures, causes strong inflammatory response, anaemia and kidney hypertrophy. In this thesis, I performed one of the first association mapping attempts on parasite resistance and tolerance in brown trout and demonstrated the possibilities as well as limitations of association analysis in natural populations. As there was very limited genomic information available for T. bryosalmonae, I also generated an annotated assembly of the parasite transcriptome. Furthermore, by combining -omic approaches with genetic mark-recapture and classical regression-based selection analysis, I demonstrated the effect of temperature-driven parasite-induced contemporary natural selection on transcript abundance and co-regulated gene networks in this wild vertebrate species. I identified several promising candidate genes involved in PKD resistance and severity in brown trout. I also characterized more than three thousand transcripts of T. bryosalmonae. Among these, I also identified four novel protein drug targets, which can help in curing the infected fish. I also showed that the myxozoan parasite induces massive cell proliferation in the fish host whose variation is associated with the survival selection on the co-regulated gene networks. The directional selection on the individual transcript abundances was weak, similar to the published selection estimates on phenotypic traits. Finally, I also discovered many transcripts exhibiting widespread signal of disruptive selection, related to host immune defence, host–pathogen interactions, cellular repair and maintenance. Overall, my thesis showcases the power of integrating ecological and genomic perspectives to gain novel insights into the functional genomic basis of resistance against a parasite, health damage (i.e., anaemia) caused by the parasite and the ongoing associated natural selection in the wild. Altogether, my thesis combines multiple levels of biological complexities and represents a significant step forward towards understanding the molecular basis of T. bryosalmonae and PKD.Isäntä-loissuhteen genomiikka ja ekologia: geenien ja transkriptomien yhdistäminen sairauksiin ja valintaan arttuvat taudit luonnonpopulaatioissa ovat tärkeitä ekologian ja evoluution tutkimuskohteita koska ne kuvaavat, kuinka loinen vaikuttaa isäntänsä kelpoisuuteen. Isännässä voi kehittyä evolutiivisia adaptaatioita loista vastaan joko resistenssin tai toleranssin kautta. Tällaisten piirteiden voidaan katsoa olevan isännässä kehittyneitä biokemiallisia ja molekyylisiä puolustusstrategioita loisia vastaan. Näihin piirteisiin liittyviä geenejä tunnetaan heikosti. Lisäksi luonnonvalinnan voimakkuutta transkription määrään ei ole tutkittu suoraan, huolimatta siitä, että geenisäätelyllä on tärkeä rooli adaptiivisessa evoluutiossa. Tässä väitöskirjassa käytin genomisia ja transkriptomisia lähestymistapoja Tetracapsuloides bryosalmonae -loistartunnan saaneen anadromisen taimenen (Salmo trutta) isäntäloisjärjestelmän tutkimiseen. Lohikaloissa T. bryosalmonae aiheuttaa lämpötilasta riippuvan sairauden, proliferatiivisen munuaissairauden (PKD). Tämä Myxozoa-pääjaksoon kuuluva loinen infektoi nuorien kalojen munuaiset ja pernan, ja aiheuttaa korkeissa lämpötiloissa voimakkaan tulehdusvasteen, anemiaa ja munuaisten liikakasvua. Tein ns. ”association mapping”-analyysin liittyen taimenen resistenssiin ja toleranssiin T. bryosalmonae-loista kohtaan, ja osoitin sekä assosiaatioanalyysin käytön mahdollisuudet että rajoitukset tutkittaessa luonnonpopulaatioita. Koska T. bryosalmonaesta oli saatavilla hyvin vähän genomista tietoa, loin myös annotoidun koosteen loisen transkriptomista. Lisäksi, yhdistämällä kolme metodia: ns. ”-omics”-lähestymistavan, geneettisen merkintäjälleenpyynnin ja klassiseen regressioon perustuvan valinta-analyysin, pystyin osoittamaan lämpötilasta riippuvaisen loisinfektion indusoiman luonnonvalinnan vaikutuksen transkription määrään ja yhteissäädeltyihin geeniverkostoihin tässä luonnossa elävässä selkärankais-lajissa. Tunnistin useita lupaavia kandidaattigeenejä, jotka liittyvät PKD-resistenssiin ja taudin vaikeusasteeseen taimenessa. Kuvasin myös yli kolmetuhatta T. bryosalmonaen transkriptia. Näiden joukosta tunnistin myös neljä uutta proteiinilääke-kohdetta, joiden avulla voidaan parantaa tartunnan saaneita kaloja. Lisäksi osoitin, että tämä Myxozoan-pääjaksoon kuuluva loinen indusoi massiivista solujen lisääntymistä kalaisännässä, ja jonka vaihtelu liittyy selviytymisvalintaan yhteissäädellyissä geeniverkostoissa. Yksittäisten transkriptien runsauteen kohdistuva suuntaava valinta oli heikkoa, ollen samantasoista kuin kirjallisuudessa esitetyt arviot fenotyyppisiin ominaisuuksiin kohdistuvasta valinnasta. Löysin myös monia transkripteja, joihin kohdistui hajoittavaa valintaa. Nämä liittyvät isännän immuunipuolustukseen, isännän ja patogeenin väliseen vuorovaikutukseen, solujen korjaamiseen ja ylläpitoon. Väitöskirjani tuo hyvin esille ekologisten ja genomisten lähestymistapojen yhdistämisestä avautuvat uudet tutkimusmahdollisuudet ja näkökulmat loisen vastustuskyvyn genomiseen ja toiminnalliseen perustaan, loisen aiheuttamaan terveyshaittaan (eli anemiaan) ja siihen liittyvään luonnonvalintaan. Väitöskirjani yhdistää monia biologian tasoja ja on merkittävä askel kohti T. bryosalmonaen ja PKD:n molekyyliperustan ymmärtämistä