Bayesian sequence learning for predicting protein cleavage points

A challenging problem in data mining is the application of efficient techniques to automatically annotate the vast databases of biological sequence data. This paper describes one such application in this area, to the prediction of the position of signal peptide cleavage points along protein sequences. It is shown that the method, based on Bayesian statistics, is comparable in terms of accuracy to the existing state-of-the-art neural network techniques while providing explanatory information for its predictions

DeepSig: Deep learning improves signal peptide detection in proteins

Motivation: The identification of signal peptides in protein sequences is an important step toward protein localization and function characterization. Results: Here, we present DeepSig, an improved approach for signal peptide detection and cleavage-site prediction based on deep learning methods. Comparative benchmarks performed on an updated independent dataset of proteins show that DeepSig is the current best performing method, scoring better than other available state-of-the-art approaches on both signal peptide detection and precise cleavage-site identification. Availability and implementation: DeepSig is available as both standalone program and web server at https://deepsig.biocomp.unibo.it. All datasets used in this study can be obtained from the same website

Plastid proteome prediction for diatoms and other algae with secondary plastids of the red lineage

The plastids of ecologically and economically important algae from phyla such as stramenopiles, dinoflagellates and cryptophytes were acquired via a secondary endosymbiosis and are surrounded by three or four membranes. Nuclear-encoded plastid-localized proteins contain N-terminal bipartite targeting peptides with the conserved amino acid sequence motif ‘ASAFAP’. Here we identify the plastid proteomes of two diatoms, Thalassiosira pseudonana and Phaeodactylum tricornutum, using a customized prediction tool (ASAFind) that identifies nuclear-encoded plastid proteins in algae with secondary plastids of the red lineage based on the output of SignalP and the identification of conserved ‘ASAFAP’ motifs and transit peptides. We tested ASAFind against a large reference dataset of diatom proteins with experimentally confirmed subcellular localization and found that the tool accurately identified plastid-localized proteins with both high sensitivity and high specificity. To identify nucleus-encoded plastid proteins of T. pseudonana and P. tricornutum we generated optimized sets of gene models for both whole genomes, to increase the percentage of full-length proteins compared with previous assembly model sets. ASAFind applied to these optimized sets revealed that about 8% of the proteins encoded in their nuclear genomes were predicted to be plastid localized and therefore represent the putative plastid proteomes of these algae

BUSCA: An integrative web server to predict subcellular localization of proteins

Here, we present BUSCA (http://busca.biocomp.unibo.it), a novel web server that integrates different computational tools for predicting protein subcellular localization. BUSCA combines methods for identifying signal and transit peptides (DeepSig and TPpred3), GPI-anchors (PredGPI) and transmembrane domains (ENSEMBLE3.0 and BetAware) with tools for discriminating subcellular localization of both globular and membrane proteins (BaCelLo, MemLoci and SChloro). Outcomes from the different tools are processed and integrated for annotating subcellular localization of both eukaryotic and bacterial protein sequences. We benchmark BUSCA against protein targets derived from recent CAFA experiments and other specific data sets, reporting performance at the state-of-the-art. BUSCA scores better than all other evaluated methods on 2732 targets from CAFA2, with a F1 value equal to 0.49 and among the best methods when predicting targets from CAFA3. We propose BUSCA as an integrated and accurate resource for the annotation of protein subcellular localization

Prediction and classification for GPCR sequences based on ligand specific features

Functional identification of G-Protein Coupled Receptors (GPCRs) is one of the current focus areas of pharmaceutical research. Although thousands of GPCR sequences are known, many of them are orphan sequences (the activating ligand is unknown). Therefore, classification methods for automated characterization of orphan GPCRs are imperative. In this study, for predicting Level 1 subfamilies of GPCRs, a novel method for obtaining class specific features, based on the existence of activating ligand specific patterns, has been developed and utilized for a majority voting classification. Exploiting the fact that there is a non-promiscuous relationship between the specific binding of GPCRs into their ligands and their functional classification, our method classifies Level 1 subfamilies of GPCRs with a high predictive accuracy between 99% and 87% in a three-fold cross validation test. The method also tells us which motifs are significant for class determination which has important design implications. The presented machine learning approach, bridges the gulf between the excess amount of GPCR sequence data and their poor functional characterization

Prediction of peptides binding to MHC class I alleles by partial periodic pattern mining

MHC (Major Histocompatibility Complex) is a key player in the immune response of an organism. It is important to be able to predict which antigenic peptides will bind to a specific MHC allele and which will not, creating possibilities for controlling immune response and for the applications of immunotherapy. However, a problem for MHC class I is the presence of bulges and loops in the peptides, changing the total length. Most machine learning methods in use today require the sequences to be of same length to successfully mine the binding motifs. We propose the use of time-based data mining methods in motif mining to be able to mine motifs position-independently. Also, the information for both binding and non-binding peptides is used on the contrary to the other methods which only rely on binding peptides. The prediction results are between 60-95% for the tested alleles

Urine peptidomic biomarkers for diagnosis of patients with systematic lupus erythematosus

Background: Systematic lupus erythematosus (SLE) is characterized with various complications which can cause serious organ damage in the human body. Despite the significant improvements in disease management of SLE patients, the non-invasive diagnosis is entirely missing. In this study, we used urinary peptidomic biomarkers for early diagnosis of disease onset to improve patient risk stratification, vital for effective drug treatment. Methods: Urine samples from patients with SLE, lupus nephritis (LN) and healthy controls (HCs) were analyzed using capillary electrophoresis coupled to mass spectrometry (CE-MS) for state-of-the-art biomarker discovery. Results: A biomarker panel made up of 65 urinary peptides was developed that accurately discriminated SLE without renal involvement from HC patients. The performance of the SLE-specific panel was validated in a multicentric independent cohort consisting of patients without SLE but with different renal disease and LN. This resulted in an area under the receiver operating characteristic (ROC) curve (AUC) of 0.80 (p < 0.0001, 95% confidence interval (CI) 0.65–0.90) corresponding to a sensitivity and a specificity of 83% and 73%, respectively. Based on the end terminal amino acid sequences of the biomarker peptides, an in silico methodology was used to identify the proteases that were up or down-regulated. This identified matrix metalloproteinases (MMPs) as being mainly responsible for the peptides fragmentation. Conclusions: A laboratory-based urine test was successfully established for early diagnosis of SLE patients. Our approach determined the activity of several proteases and provided novel molecular information that could potentially influence treatment efficacy

An extra dimension in protein tagging by quantifying universal proteotypic peptides using targeted proteomics

The use of protein tagging to facilitate detailed characterization of target proteins has not only revolutionized cell biology, but also enabled biochemical analysis through efficient recovery of the protein complexes wherein the tagged proteins reside. The endogenous use of these tags for detailed protein characterization is widespread in lower organisms that allow for efficient homologous recombination. With the recent advances in genome engineering, tagging of endogenous proteins is now within reach for most experimental systems, including mammalian cell lines cultures. In this work, we describe the selection of peptides with ideal mass spectrometry characteristics for use in quantification of tagged proteins using targeted proteomics. We mined the proteome of the hyperthermophile Pyrococcus furiosus to obtain two peptides that are unique in the proteomes of all known model organisms (proteotypic) and allow sensitive quantification of target proteins in a complex background. By combining these 'Proteotypic peptides for Quantification by SRM' (PQS peptides) with epitope tags, we demonstrate their use in co-immunoprecipitation experiments upon transfection of protein pairs, or after introduction of these tags in the endogenous proteins through genome engineering. Endogenous protein tagging for absolute quantification provides a powerful extra dimension to protein analysis, allowing the detailed characterization of endogenous proteins

Deep proteogenomics; high throughput gene validation by multidimensional liquid chromatography and mass spectrometry of proteins from the fungal wheat pathogen Stagonospora nodorum

BACKGROUND: Stagonospora nodorum, a fungal ascomycete in the class dothideomycetes, is a damaging pathogen of wheat. It is a model for necrotrophic fungi that cause necrotic symptoms via the interaction of multiple effector proteins with cultivar-specific receptors. A draft genome sequence and annotation was published in 2007. A second-pass gene prediction using a training set of 795 fully EST-supported genes predicted a total of 10762 version 2 nuclear-encoded genes, with an additional 5354 less reliable version 1 genes also retained. RESULTS: In this study, we subjected soluble mycelial proteins to proteolysis followed by 2D LC MALDI-MS/MS. Comparison of the detected peptides with the gene models validated 2134 genes. 62% of these genes (1324) were not supported by prior EST evidence. Of the 2134 validated genes, all but 188 were version 2 annotations. Statistical analysis of the validated gene models revealed a preponderance of cytoplasmic and nuclear localised proteins, and proteins with intracellularassociated GO terms. These statistical associations are consistent with the source of the peptides used in the study. Comparison with a 6-frame translation of the S. nodorum genome assembly confirmed 905 existing gene annotations (including 119 not previously confirmed) and provided evidence supporting 144 genes with coding exon frameshift modifications, 604 genes with extensions of coding exons into annotated introns or untranslated regions (UTRs), 3 new gene annotations which were supported by tblastn to NR, and 44 potential new genes residing within un-assembled regions of the genome. CONCLUSION: We conclude that 2D LC MALDI-MS/MS is a powerful, rapid and economical tool to aid in the annotation of fungal genomic assemblies