Human Protein Sequences Research Articles

The Poly-E motif in Titin's PEVK region undergoes pH dependent conformational changes

The muscle protein titin plays a crucial role in passive elasticity and the disordered PEVK region within titin is central to that function. The PEVK region is so named due to its high proline, glutamate, valine and lysine content and the high charge density in this region results in a lack of organized structure within this domain. The PEVK region is highly extensible but the molecular interactions that contribute to the elastic nature of the PEVK still remain poorly described. The PEVK region is formed by two unique sequence motifs. The PPAK motif is a 26 to 28 amino acid sequence that contains a mixture of charged and hydrophobic residues and is the primary building block for the PEVK region. Poly-E sequence motifs vary in length and contain clusters of 3–4 glutamic acids distributed throughout the motif. In this study, we derived two 28-residue peptides from the human titin protein sequence and measured their structural characteristics over a range of pHs. Our results demonstrate that the poly-E peptide undergoes a shift from a more rigid and elongated state to a more collapsed state as pH decreases with the midpoint of this transition being at pH ~5.5. Interestingly, a similar conformational shift is not observed in the PPAK peptide. These results suggest that the poly-E motif might provide a nucleating site for the PEVK when the muscle is not in an extended state.

Computational Identification of Human Biological Processes and Protein Sequence Motifs Putatively Targeted by SARS-CoV-2 Proteins Using Protein-Protein Interaction Networks.

While the COVID-19 pandemic is causing important loss of life, knowledge of the effects of the causative SARS-CoV-2 virus on human cells is currently limited. Investigating protein–protein interactions (PPIs) between viral and host proteins can provide a better understanding of the mechanisms exploited by the virus and enable the identification of potential drug targets. We therefore performed an in-depth computational analysis of the interactome of SARS-CoV-2 and human proteins in infected HEK 293 cells published by Gordon et al. (Nature2020, 583, 459–468) to reveal processes that are potentially affected by the virus and putative protein binding sites. Specifically, we performed a set of network-based functional and sequence motif enrichment analyses on SARS-CoV-2-interacting human proteins and on PPI networks generated by supplementing viral-host PPIs with known interactions. Using a novel implementation of our GoNet algorithm, we identified 329 Gene Ontology terms for which the SARS-CoV-2-interacting human proteins are significantly clustered in PPI networks. Furthermore, we present a novel protein sequence motif discovery approach, LESMoN-Pro, that identified 9 amino acid motifs for which the associated proteins are clustered in PPI networks. Together, these results provide insights into the processes and sequence motifs that are putatively implicated in SARS-CoV-2 infection and could lead to potential therapeutic targets.

Prediction of the Receptorome for the Human-Infecting Virome.

The virus receptors are key for the viral infection of host cells. Identification of the virus receptors is still challenging at present. Our previous study has shown that human virus receptor proteins have some unique features including high N-glycosylation level, high number of interaction partners and high expression level. Here, a random-forest model was built to identify human virus receptorome from human cell membrane proteins with an accepted accuracy based on the combination of the unique features of human virus receptors and protein sequences. A total of 1424 human cell membrane proteins were predicted to constitute the receptorome of the human-infecting virome. In addition, the combination of the random-forest model with protein–protein interactions between human and viruses predicted in previous studies enabled further prediction of the receptors for 693 human-infecting viruses, such as the enterovirus, norovirus and West Nile virus. Finally, the candidate alternative receptors of the SARS-CoV-2 were also predicted in this study. As far as we know, this study is the first attempt to predict the receptorome for the human-infecting virome and would greatly facilitate the identification of the receptors for viruses.Electronic supplementary materialThe online version of this article (10.1007/s12250-020-00259-6) contains supplementary material, which is available to authorized users.

Using Language Representation Learning Approach to Efficiently Identify Protein Complex Categories in Electron Transport Chain.

We herein proposed a novel approach based on the language representation learning method to categorize electron complex proteins into 5 types. The idea is stemmed from the the shared characteristics of human language and protein sequence language, thus advanced natural language processing techniques were used for extracting useful features. Specifically, we employed transfer learning and word embedding techniques to analyze electron complex sequences and create efficient feature sets before using a support vector machine algorithm to classify them. During the 5-fold cross-validation processes, seven types of sequence-based features were analyzed to find the optimal features. On an average, our final classification models achieved the accuracy, specificity, sensitivity, and MCC of 96 %, 96.1 %, 95.3 %, and 0.86, respectively on cross-validation data. For the independent test data, those corresponding performance scores are 95.3 %, 92.6 %, 94 %, and 0.87. We concluded that using feature extracted using these representation learning methods, the prediction performance of simple machine learning algorithm is on par with existing deep neural network method on the task of categorizing electron complexes while enjoying a much faster way for feature generation. Furthermore, the results also showed that the combination of features learned from the representation learning methods and sequence motif counts helps yield better performance.

Dissecting the potential role of hepatitis E virus ORF1 nonstructural gene in cross-species infection by using intergenotypic chimeric viruses.

Hepatitis E virus (HEV) infects humans and more than a dozen other animal species. We previously showed that open reading frame 2 (ORF2) and ORF3 are apparently not involved in HEV cross-species infection, which infers that the ORF1 may contribute to host tropism. In this study, we utilize the genomic backbone of HEV-1 which only infects humans to construct a panel of intergenotypic chimeras in which the entire ORF1 gene or its functional domains were swapped with the corresponding regions from HEV-3 that infects both humans and pigs. We demonstrated that the chimeric HEVs were replication competent in human liver cells. Subsequently, we intrahepatically inoculated the RNA transcripts of chimeras into pigs to determine if the swapped ORF1 regions confer the chimeras' ability to infect pigs. We showed that there was no evidence of infectivity in pigs for any of the chimeras. We also investigated the role of human ribosome protein sequence S17, which expanded host range in cultured cells, in HEV cross-species infection. We demonstrated that S17 insertion in HEV ORF1 did not abolish HEV replication competency in vitro, but also did not expand HEV host tropism in vivo. The results highlight the complexity of the underlying mechanism of HEV cross-species infection.

Improved Prediction of Protein-Protein Interaction Mapping on Homo Sapiens by Using Amino Acid Sequence Features in a Supervised Learning Framework.

Protein-Protein Interaction (PPI) has emerged as a key role in the control of many biological processes including protein function, disease incidence, and therapy design. However, the identification of PPI by wet lab experiment is a challenging task, since it is laborious, time consuming and expensive. Therefore, computational prediction of PPI is now given emphasis before going to the experimental validation, since it is simultaneously less laborious, time saver and cost minimizer. The objective of this study is to develop an improved computational method for PPI prediction mapping on Homo sapiens by using the amino acid sequence features in a supervised learning framework. The experimentally validated 91 positive-PPI pairs of human protein sequences were collected from IntAct Molecular Interaction Database. Then we constructed three balanced datasets with ratios 1:1, 1:2 and 1:3 of positive and negative PPI samples. Then we partitioned each dataset into training (80%) and independent test (20%) datasets. Again each training dataset was partitioned into four mutually exclusive groups of equal sizes for interchanging each group with independent test group to perform 5-fold cross validation (CV). Then we trained candidate seven classifiers (NN, SVM, LR, NB, KNN, AB and RF) with each ratio case to obtain the better PPI predictor by comparing their performance scores. The random forest (RF) based predictor that was trained with 1:2 ratio of positive-PPI and negative-PPI samples based on AAC encoding features provided the most accurate PPI prediction by producing the highest average performance scores of accuracy (93.50%), sensitivity (95.0%), MCC (85.2%), AUC (0.941) and pAUC (0.236) with the 5-fold cross-validation. It also achieved the highest average performance scores of accuracy (92.0%), sensitivity (94.0%), MCC (83.6%), AUC (0.922) and pAUC (0.207) with the independent test datasets in a comparison of the other candidate and existing predictors. The final resultant prediction strongly recommend that the RF based predictor is a better prediction model of PPI mapping on Homo sapiens.

LIST-S2: taxonomy based sorting of deleterious missense mutations across species.

The separation of deleterious from benign mutations remains a key challenge in the interpretation of genomic data. Computational methods used to sort mutations based on their potential deleteriousness rely largely on conservation measures derived from sequence alignments. Here, we introduce LIST-S2, a successor to our previously developed approach LIST, which aims to exploit local sequence identity and taxonomy distances in quantifying the conservation of human protein sequences. Unlike its predecessor, LIST-S2 is not limited to human sequences but can assess conservation and make predictions for sequences from any organism. Moreover, we provide a web-tool and downloadable software to compute and visualize the deleteriousness of mutations in user-provided sequences. This web-tool contains an HTML interface and a RESTful API to submit and manage sequences as well as a browsable set of precomputed predictions for a large number of UniProtKB protein sequences of common taxa. LIST-S2 is available at: https://list-s2.msl.ubc.ca/

The Absence of Calponin 2 in Rabbits Suggests Caution in Choosing Animal Models.

While the rapid development of CRISPR/CAS9 technology has allowed for readily performing site-specific genomic editing in non-rodent species, an emerging challenge is to select the most suitable species to generate animal models for the study of human biology and diseases. Improving CRISPR/CAS9 methodology for more effective and precise editing in the rabbit genome to replicate human disease is an active area of biomedical research. Although rabbits are more closely related to humans than mice (based on DNA sequence analysis), our whole-genome protein database search revealed that rabbits have more missing human protein sequences than mice. Hence, precisely replicating human diseases in rabbits requires further consideration, especially in studies involving essential functions of the missing proteins. For example, rabbits lack calponin 2, an actin-associated cytoskeletal protein that is important in the pathogenesis of inflammatory arthritis, atherosclerosis, and calcific aortic valve disease. The justification of using rabbits as models for human biomedical research is based on their larger size and their closer phylogenetic distance to humans (based on sequence similarity of conserved genes), but this may be misleading. Our findings, which consider whole-genome protein profiling together with actual protein expressions, serve as a warning to the scientific community to consider overall conservation as well as the conservation of specific proteins when choosing an animal model to study a particular aspect of human biology prior to investing in genetic engineering.

18 A HUMAN-DERIVED 5-MER PEPTIDE (MTADV), WHICH RESTRICTIVELY ALLEVIATES THE PRO-INFLAMMATORY ACTIVITY OF SERUM AMYLOID A (SAA), SUBSTANTIALLY AMELIORATES IBD PATHOLOGY: NEW POTENTIAL DRUG (MTADV) AND THERAPEUTIC TARGET CANDIDATE (SAA) FOR IBD

Abstract A human anti-inflammatory 5-MER peptide MTADV (methionine-threonine-alanine-aspartic acid-valine) derived from a sequence of a pro-inflammatory CD44 variant was first described by us in Nedvetzki et al., J. Clin. Invest. 111,1211-1220, 2003. A NCBI BLAST RefSeq protein sequence database, that contains at present 81,347 human protein sequences, reveals that only one additional protein contains this unique MTADV sequence of alternating hydrophobic and hydrophilic amino acids. This human peptide displays an efficient anti-inflammatory effect in Inflammatory Bowel Disease (IBD), Rheumatoid Arthritis and Multiple sclerosis mouse models, all share amyloid proteins in the relevant target organs. We show here that the 5-MER peptide, administered after the onset of disease,either by SC injections or oral delivery, inhibited the pathological activity of two IBD models (TNBS and DSS), as indicated by histopathology analysis (Ameho’s score) or Bioluminescence Imaging, sensing the release of Reactive Oxdative Species (ROS), which reflect colon inflammation (Fig 1). The 5-MER MTADV peptide, while inhibiting chronic inflammation, neither interferes with normal immune responses nor induce neutralizing antibodies. A pull-down experiment with mass spectrometric analysis revealed that the 5-MER peptide specifically binds to serum amyloid A (SAA), a stress pro-inflammatory protein, which generates aggregated amyloid deposits in the IBD colon (de Villiers at al Cytokine 12:1337-1347,2000). Our studies further provide in vitro evidence, strengthened by in vivo experiments, that SAA is a significant target of this pentamer. To this end, the 5-MER peptide (but not the corresponding scrambled peptide) inhibits the release of the pro-inflammatory cytokines IL-6 and IL-1β from SAA-activated fibroblasts. Furthermore, the 5-MER peptide was found to retard the early stages of amyloid-type aggregation of SAA in solution (Fig 2). Adopting the β-sheet conformation, MTADV would display opposing hydrophobic and hydrophilic faces that could interact with the β-sheet-forming amyloidogenic sequences in SAA. This suggests that the mechanism of action for the 5-MER peptide in vivo may depend on its ability to slow the aggregation of SAA, thus reduce its contribution to chronic inflammation. Finally, using bioinformatics and qRT-PCR, we have found the pentamer up-regulates set of genes involved in resistance to chronic inflammations. Hence, our study provides both a new potential drug (MTADV) and a new therapeutic target candidate (SAA) for IBD.

Identification of the autophagy pathway in a mollusk bivalve, Crassostrea gigas

ABSTRACT The Pacific oyster, Crassostrea gigas, is a mollusk bivalve commercially important as a food source. Pacific oysters are subjected to stress and diseases during culture. The autophagy pathway is involved in numerous cellular processes, including responses to starvation, cell death, and microorganism elimination. Autophagy also exists in C. gigas, and plays a role in the immune response against infections. Although this process is well-documented and conserved in most animals, it is still poorly understood in mollusks. To date, no study has provided a complete overview of the molecular mechanism of autophagy in mollusk bivalves. In this study, human and yeast ATG protein sequences and public databases (Uniprot and NCBI) were used to identify protein members of the C. gigas autophagy pathway. A total of 35 autophagy related proteins were found in the Pacific oyster. RACE-PCR was performed on several genes. Using molecular (real-time PCR) and protein-based (western blot and immunohistochemistry) approaches, the expression and localization of ATG12, ATG9, BECN1, MAP1LC3, MTOR, and SQSTM1, was investigated in different tissues of the Pacific oyster. Comparison with human and yeast counterparts demonstrated a high homology with the human autophagy pathway. The results also demonstrated that the key autophagy genes and their protein products were expressed in all the analyzed tissues of C. gigas. This study allows the characterization of the complete C. gigas autophagy pathway for the first time. Abbreviations: ATG: autophagy related; Atg1/ULK: unc-51 like autophagy activating kinase; ATG7: autophagy related 7; ATG9: autophagy related 9; ATG12: autophagy related 12; BECN1: beclin 1; BSA: bovine serum albumin; cDNA: complementary deoxyribonucleic acid; DNA: deoxyribonucleic acid; GABARAP: GABA type A receptor-associated protein; IHC: immunohistochemistry; MAP1LC3/LC3/Atg8: microtubule associated protein 1 light chain 3; MTOR: mechanistic target of rapamycin kinase; NCBI: national center for biotechnology information; ORF: open reading frame; PBS: phosphate-buffered saline; PCR: polymerase chain reaction; PtdIns3K: class III phosphatidylinositol 3-kinase; RACE-PCR: rapid amplification of cDNA-ends by polymerase chain reaction; RNA: ribonucleic acid; SQSTM1: sequestosome 1; Uniprot: universal protein resource; WIPI: WD repeat domain, phosphoinositide interacting.

The Occurrence of Genetic Recombination between Viruses and Human, it's Possible Influence on Vaccination

Relevance. The genetic recombination between viruses and men is known long ago. It can be divided on relict and ontogenic ones. For the host the recombination may display different consequences the nature of which is not exposed explicitly.Aim is to analyze (on the base of computer comparison of the primary structure of viral and human proteins ) the occurrence of twodirectional recombination by small genome fragments between viruses and men and describe its possible after-effects.Materials and methods. For this computer study human and virus protein sequences were used from data bases available in INTERNET.Results. It was indicated that recombination (cryptical and explicit) by small genome fragments between viruses and men occurred many times in the past and many viruses pathogenic for men were involved in it.Conclusion. The bioinformatics approach allows to look at the past of viruses and men and find the traces of genetic information changes between them that may predetermine the effects of vaccines and diagnostic immune tests.

Modelling of pathogen-host systems using deeper ORF annotations and transcriptomics to inform proteomics analyses

The Zika virus is a flavivirus that can cause fulminant outbreaks and lead to Guillain-Barré syndrome, microcephaly and fetal demise. Like other flaviviruses, the Zika virus is transmitted by mosquitoes and provokes neurological disorders. Despite its risk to public health, no antiviral nor vaccine are currently available. In the recent years, several studies have set to identify human host proteins interacting with Zika viral proteins to better understand its pathogenicity. Yet these studies used standard human protein sequence databases. Such databases rely on genome annotations, which enforce a minimal open reading frame (ORF) length criterion. An ever-increasing number of studies have demonstrated the shortcomings of such annotation, which overlooks thousands of functional ORFs. Here we show that the use of a customized database including currently non-annotated proteins led to the identification of 4 alternative proteins as interactors of the viral capsid and NS4A proteins. Furthermore, 12 alternative proteins were identified in the proteome profiling of Zika infected monocytes, one of which was significantly up-regulated. This study presents a computational framework for the re-analysis of proteomics datasets to better investigate the viral-host protein interplays upon infection with the Zika virus.

223 Lesch-Nyhan syndrome: from patient to mouse model

Abstract Introduction. HPRT1 is a Hypoxanthine-guanine phosphoribosyltransferase (HGPRT) coding gene, mutations of which cause, among other diseases, Lesch-Nyhan syndrome. LNS is a severe X-linked recessive neurological disorder. Alignment shows 98.6% homology of human and murine protein sequences. Objectives. We assume that murine model of this human disease should be developed. So, our purpose is to create a transgenic mouse model of Lesch-Nyhan syndrome by CRISPR/Cas9 genome editing system. Methods. The BLAST was used to find homologous mutation in murine HPRT1 gene comparing to Human one. X-ray crystallographic structural model of the HPRT1 was used as template for M4T Raptor algorithm to generate predicted structure of mutated protein. Models were visualized in PyMol (Schrodinger, Portland, OR). Lack of enzymatic activity of the HGPRT could be caused via troubled homodimerization. Absence of the aliphatic Valine could be the reason of the hindered monomers interaction. Genetic construction based on the px330 plasmid was brought using microinjection method in mouse fertilized ovum for producing primary transgenic organisms. Results. The CRISPR/Cas9 system was specifically designed to carry out mutations in HPRT. The resulting genetic construct was introduced into the fertilized mouse ovum to obtain primary transgenic organisms. This mouse was obtained at the Institute of Biology of the Russian Academy of Sciences. Conclusion. Homologous mutation in human and murine HPRT1 gene resulting into comparable conformational change in the protein model structure was revealed, so murine personalized model of the Lesch-Nyhan syndrome could be developed. Structural changes can be further studied to provide treatment strategy for people suffering from Lesch-Nyhan syndrome. This study was supported by Russian Science Foundation (Grant #17-75-20249).

Learning distributed representations of RNA and protein sequences and its application for predicting lncRNA-protein interactions

The long noncoding RNAs (lncRNAs) are ubiquitous in organisms and play crucial role in a variety of biological processes and complex diseases. Emerging evidences suggest that lncRNAs interact with corresponding proteins to perform their regulatory functions. Therefore, identifying interacting lncRNA-protein pairs is the first step in understanding the function and mechanism of lncRNA. Since it is time-consuming and expensive to determine lncRNA-protein interactions by high-throughput experiments, more robust and accurate computational methods need to be developed. In this study, we developed a new sequence distributed representation learning based method for potential lncRNA-Protein Interactions Prediction, named LPI-Pred, which is inspired by the similarity between natural language and biological sequences. More specifically, lncRNA and protein sequences were divided into k-mer segmentation, which can be regard as “word” in natural language processing. Then, we trained out the RNA2vec and Pro2vec model using word2vec and human genome-wide lncRNA and protein sequences to mine distribution representation of RNA and protein. Then, the dimension of complex features is reduced by using feature selection based on Gini information impurity measure. Finally, these discriminative features are used to train a Random Forest classifier to predict lncRNA-protein interactions. Five-fold cross-validation was adopted to evaluate the performance of LPI-Pred on three benchmark datasets, including RPI369, RPI488 and RPI2241. The results demonstrate that LPI-Pred can be a useful tool to provide reliable guidance for biological research.

In silico Design of an Epitope-Based Vaccine Ensemble for Chagas Disease.

Trypanosoma cruzi infection causes Chagas disease, which affects 7 million people worldwide. Two drugs are available to treat it: benznidazole and nifurtimox. Although both are efficacious against the acute stage of the disease, this is usually asymptomatic and goes undiagnosed and untreated. Diagnosis is achieved at the chronic stage, when life-threatening heart and/or gut tissue disruptions occur in ~30% of those chronically infected. By then, the drugs' efficacy is reduced, but not their associated high toxicity. Given current deficiencies in diagnosis and treatment, a vaccine to prevent infection and/or the development of symptoms would be a breakthrough in the management of the disease. Current vaccine candidates are mostly based on the delivery of single antigens or a few different antigens. Nevertheless, due to the high biological complexity of the parasite, targeting as many antigens as possible would be desirable. In this regard, an epitope-based vaccine design could be a well-suited approach. With this aim, we have gone through publicly available databases to identify T. cruzi epitopes from several antigens. By means of a computer-aided strategy, we have prioritized a set of epitopes based on sequence conservation criteria, projected population coverage of Latin American population, and biological features of their antigens of origin. Fruit of this analysis, we provide a selection of CD8+ T cell, CD4+ T cell, and B cell epitopes that have <70% identity to human or human microbiome protein sequences and represent the basis toward the development of an epitope-based vaccine against T. cruzi.

Protein Found at the Scene of the Crime: The Potential for Using Proteomics for Identification

Hair has long been collected from crime scenes as part of trace evidence. Originally, hair was used for some exclusionary purposes — only general qualities about an unknown source could be determined. Eventually, DNA was used to help identify the source but only if the root was still attached. Within the last two years, however, two major studies have used proteomics — the study of human protein sequences — to extract and identify protein sequences in an unknown source in order to match it to a known source. These two studies support the same hypothesis: proteomics is currently a viable method for narrowing down the source of the hair and will soon be able to identify an individual source. While the science is about a decade away from being comparable to nuclear DNA, the potential of proteomics is undeniable. This paper explores the current status, methods, and future of the science as well as the impact it may have on forensic investigation and criminal prosecution. This paper also explores the potential admissibility of expert testimony on proteomics for identification.

Human Protein Sequence Classification using Machine Learning and Statistical Classification Techniques

In the field of computational biology, to gauge the meaningful and accurate feature for protein function predications, either the profile-based protein data or sequence-based data has been used. As we know that the prediction of enzyme class from an unknown protein is most interacted research in the current era. In this context, machine learning and statistical classification technique has been used. In this article, we have use six different machine learning and statistical classification technique such as CRT, QUEST, CHAID, C5.0, ANN and SVM for classification of 4314 number of human protein sequence data. These data are extracted form UniprotKB databank with the help of PROFEAT server. The extracted data are categorized in seven different classes. To manipulate the high dimensional protein sequence data with some missing value, the SPSS has been used for classification and estimation of the performance of classification technique. The experimental results highlight that the class C4, C5, C6 and C7 data are imbalanced that affect the overall performance of classification technique. This article provides an extensive comparative analysis of different classification technique on sequence-based protein data. The experimental analysis highlights that the SVM and C5.0 classification technique gives better result than others and can be used for protein classification and predictions.

Molecular characterization and protein structure prediction of heat shock transcriptional factors in goat (Capra hircus) and sheep (Ovis aries)

The heat shock factors are important as they are master regulator of heat shock response. There are only few mammalian HSFs which have been characterized, namely HSF-1, HSF-2, HSF-4 and HSF-5. The present study was aimed to clone and sequence characterize the partial open reading frames (ORFs) of HSF-2 and HSF-5 gene from cDNA isolated from testicular tissue of sheep (Macheri) and goat (Beetal). The partial ORFs of HSF-2 gene was observed to be 1627 bp in sheep and 1179 bp in goat and for HSF-5 it is 1137 bp in sheep and 1027 bp in goat. HSF-2 and HSF-5 encode a putative protein of 593 and 461 amino acid in goat and 568 and 553 amino acid in sheep, respectively. Phylogenetic analysis between the different orthologs suggested that these proteins are conserved from bovine to humans as well as in other mammals. Further, domain analyses using PredictNLS, MARCOIL and NetNES revealed that the members of HSF-2 protein orthologs contained all major domains, i.e., DNA-binding domain (DBD) and oligomerization domain (HR-A/B, and HR-C). The 3D structure of sheep and goat HSF-2 protein was predicted using SWISS-MODEL, which showed similar confirmation with the human HSF-2 protein sequence showing functional similarity between them.

Exploiting orthology and de novo transcriptome assembly to refine target sequence information

BackgroundThe ability to generate recombinant drug target proteins is important for drug discovery research as it facilitates the investigation of drug-target-interactions in vitro. To accomplish this, the target’s exact protein sequence is required. Public databases, such as Ensembl, UniProt and RefSeq, are extensive protein and nucleotide sequence repositories. However, many sequences for non-human organisms are predicted by computational pipelines and may thus be incomplete or incorrect. This could lead to misinterpreted experimental outcomes due to gaps or errors in orthologous drug target sequences. Transcriptome analysis by RNA-Seq has been established as a standard method for gene expression analysis. Apart from this common application, paired-end RNA-Seq data can also be used to obtain full coverage cDNA sequences via de novo transcriptome assembly.MethodsTo assess whether de novo transcriptome assemblies can be used to determine a protein’s sequence by searching the assembly for a known orthologous sequence, we generated 3 × 6 = 18 tissue specific assemblies (three organs: brain, kidney and liver; six species: human, mouse, rat, dog, pig and cynomolgus monkey). These assemblies and the manually curated human protein sequences from UniProtKB/Swiss-Prot were used in a reciprocal BLAST search to identify best matching hits.We automated and generalised our approach and present the a&o-tool, a workflow which exploits de novo assemblies of paired-end RNA-Seq data and orthology information for target sequence validation and refinement across related species. Furthermore, the a&o-tool extracts best hits’ sequences from a reciprocal BLAST search, translates them into protein sequences, computes a multiple sequence alignment and quantifies the refinement.ResultsFor the three human assemblies we observed a hit rate greater than 60% with 100% sequence coverage and identity. For assemblies from the other species we observed similar hit rates and coverage with highest identities for cynomolgus monkey.ConclusionsIn summary, we show how to refine protein sequences using RNA-Seq data and sequence information from closely related species. With the a&o-tool we provide a fully automated pipeline to perform refinement including cDNA translation and multiple sequence alignment for visual inspection. The major prerequisite for applying the a&o-tool is high quality sequencing data.

AMYCO: evaluation of mutational impact on prion-like proteins aggregation propensity

BackgroundAround 1% of human proteins are predicted to contain a disordered and low complexity prion-like domain (PrLD). Mutations in PrLDs have been shown promote a transition towards an aggregation-prone state in several diseases.ResultsRecently, we have shown that an algorithm that considers the effects of mutations on PrLDs composition, as well as on localized amyloid propensity can predict the impact of these amino acid changes on protein intracellular aggregation. In this application note, we implement this concept into the AMYCO web server, a refined algorithm that forecasts the influence of amino acid changes in prion-like proteins aggregation propensity better than state-of-the-art predictors.ConclusionsThe AMYCO web server allows for a fast and automated evaluation of the effect of mutations on the aggregation properties of prion-like proteins. This might uncover novel disease-linked amino acid changes in the sequences of human prion-like proteins. Additionally, it can find application in the in silico design of synthetic prion-like proteins with tuned aggregation propensities for different purposes. AMYCO does not require previous registration and is freely available to all users at: http://bioinf.uab.cat/amyco/.