Open Targets Platform: supporting systematic drug-target identification and prioritisation.

The Open Targets Platform (https://www.targetvalidation.org/) provides users with a queryable knowledgebase and user interface to aid systematic target identification and prioritisation for drug discovery based upon underlying evidence. It is publicly available and the underlying code is open source. Since our last update two years ago, we have had 10 releases to maintain and continuously improve evidence for target-disease relationships from 20 different data sources. In addition, we have integrated new evidence from key datasets, including prioritised targets identified from genome-wide CRISPR knockout screens in 300 cancer models (Project Score), and GWAS/UK BioBank statistical genetic analysis evidence from the Open Targets Genetics Portal. We have evolved our evidence scoring framework to improve target identification. To aid the prioritisation of targets and inform on the potential impact of modulating a given target, we have added evaluation of post-marketing adverse drug reactions and new curated information on target tractability and safety. We have also developed the user interface and backend technologies to improve performance and usability. In this article, we describe the latest enhancements to the Platform, to address the fundamental challenge that developing effective and safe drugs is difficult and expensive.

Open Targets Platform: new developments and updates two years on.

The Open Targets Platform integrates evidence from genetics, genomics, transcriptomics, drugs, animal models and scientific literature to score and rank target-disease associations for drug target identification. The associations are displayed in an intuitive user interface (https://www.targetvalidation.org), and are available through a REST-API (https://api.opentargets.io/v3/platform/docs/swagger-ui) and a bulk download (https://www.targetvalidation.org/downloads/data). In addition to target-disease associations, we also aggregate and display data at the target and disease levels to aid target prioritisation. Since our first publication two years ago, we have made eight releases, added new data sources for target-disease associations, started including causal genetic variants from non genome-wide targeted arrays, added new target and disease annotations, launched new visualisations and improved existing ones and released a new web tool for batch search of up to 200 targets. We have a new URL for the Open Targets Platform REST-API, new REST endpoints and also removed the need for authorisation for API fair use. Here, we present the latest developments of the Open Targets Platform, expanding the evidence and target-disease associations with new and improved data sources, refining data quality, enhancing website usability, and increasing our user base with our training workshops, user support, social media and bioinformatics forum engagement.

TCTN2: a novel tumor marker with oncogenic properties.

Tectonic family member 2 (TCTN2) encodes a transmembrane protein that belongs to the tectonic family, which is involved in ciliary functions. Previous studies have demonstrated the role of tectonics in regulating a variety of signaling pathways at the transition zone of cilia. However, the role of tectonics in cancer is still unclear. Here we identify that TCTN2 is overexpressed in colorectal, lung and ovary cancers. We show that different cancer cell lines express the protein that localizes at the plasma membrane, facing the intracellular milieu. TCTN2 over-expression in cancer cells resulted in an increased ability to form colonies in an anchorage independent way. On the other hand, downregulation of TCTN2 using targeted epigenetic editing in cancer cells significantly reduced colony formation, cell invasiveness, increased apoptosis and impaired assembly of primary cilia. Taken together, our results indicate that TCTN2 acts as an oncogene, making it an interesting cancer-associated protein and a potential candidate for therapeutic applications.

Open Targets: a platform for therapeutic target identification and validation.

We have designed and developed a data integration and visualization platform that provides evidence about the association of known and potential drug targets with diseases. The platform is designed to support identification and prioritization of biological targets for follow-up. Each drug target is linked to a disease using integrated genome-wide data from a broad range of data sources. The platform provides either a target-centric workflow to identify diseases that may be associated with a specific target, or a disease-centric workflow to identify targets that may be associated with a specific disease. Users can easily transition between these target- and disease-centric workflows. The Open Targets Validation Platform is accessible at https://www.targetvalidation.org.

Recombinant outer membrane vesicles carrying Chlamydia muridarum HtrA induce antibodies that neutralize chlamydial infection in vitro.

BACKGROUND: Outer membrane vesicles (OMVs) are spheroid particles released by all Gram-negative bacteria as a result of the budding out of the outer membrane. Since they carry many of the bacterial surface-associated proteins and feature a potent built-in adjuvanticity, OMVs are being utilized as vaccines, some of which commercially available. Recently, methods for manipulating the protein content of OMVs have been proposed, thus making OMVs a promising platform for recombinant, multivalent vaccines development. METHODS: Chlamydia muridarum DO serine protease HtrA, an antigen which stimulates strong humoral and cellular responses in mice and humans, was expressed in Escherichia coli fused to the OmpA leader sequence to deliver it to the OMV compartment. Purified OMVs carrying HtrA (CM rHtrA-OMV) were analyzed for their capacity to induce antibodies capable of neutralizing Chlamydia infection of LLC-MK2 cells in vitro. RESULTS: CM rHtrA-OMV immunization in mice induced antibodies that neutralize Chlamydial invasion as judged by an in vitro infectivity assay. This was remarkably different from what observed with an enzymatically functional recombinant HtrA expressed in, and purified from the E. coli cytoplasm (CM rHtrA). The difference in functionality between anti-CM rHtrA and anti-CM rHtrA-OMV antibodies was associated to a different pattern of protein epitopes recognition. The epitope recognition profile of anti-CM HtrA-OMV antibodies was similar to that induced in mice during Chlamydial infection. CONCLUSIONS: When expressed in OMVs HtrA appears to assume a conformation similar to the native one and this results in the elicitation of functional immune responses. These data further support the potentiality of OMVs as vaccine platform.

A novel polyclonal antibody library for expression profiling of poorly characterized, membrane and secreted human proteins.

The YOMICS™ antibody library (http://www.yomics.com/) presented in this article is a new collection of 1559 murine polyclonal antibodies specific for 1287 distinct human proteins. This antibody library is designed to target marginally characterized membrane-associated and secreted proteins. It was generated against human proteins annotated as transmembrane or secreted in GenBank, EnsEMBL, Vega and Uniprot databases, described in no or very few dedicated PubMed-linked publications. The selected proteins/protein regions were expressed in E. coli, purified and used to raise antibodies in the mouse. The capability of YOMICS™ antibodies to specifically recognize their target proteins either as recombinant form or as expressed in cells and tissues was confirmed through several experimental approaches, including Western blot, confocal microscopy and immunohistochemistry (IHC). Moreover, to show the applicability of the library for biomarker investigation by IHC, five antibodies against proteins either known to be expressed in some cancers or homologous to tumor-associated proteins were tested on tissue microarrays carrying tumor and normal tissues from breast, colon, lung, ovary and prostate. A consistent differential expression in cancer was observed. Our results indicate that the YOMICS™ antibody library is a tool for systematic protein expression profile analysis that nicely complements the already available commercial antibody collections.

Improving the prediction of disulfide bonds in Eukaryotes with machine learning methods and protein subcellular localization.

MOTIVATION: Disulfide bonds stabilize protein structures and play relevant roles in their functions. Their formation requires an oxidizing environment and their stability is consequently depending on the redox ambient potential, which may differ according to the subcellular compartment. Several methods are available to predict cysteine-bonding state and connectivity patterns. However, none of them takes into consideration the relevance of protein subcellular localization. RESULTS: Here we develop DISLOCATE, a two-step method based on machine learning models for predicting both the bonding state and the connectivity patterns of cysteine residues in a protein chain. We find that the inclusion of protein subcellular localization improves the performance of these predictive steps by 3 and 2 percentage points, respectively. When compared with previously developed methods for predicting disulfide bonds from sequence, DISLOCATE improves the overall performance by more than 10 percentage points. AVAILABILITY: The method and the dataset are available at the Web page http://www.biocomp.unibo.it/savojard/Dislocate.html. GRHCRF code is available at http://www.biocomp.unibo.it/savojard/biocrf.html. CONTACT: piero.fariselli@unibo.it.

MemPype: a pipeline for the annotation of eukaryotic membrane proteins.

MemPype is a Python-based pipeline including previously published methods for the prediction of signal peptides (SPEP), glycophosphatidylinositol (GPI) anchors (PredGPI), all-alpha membrane topology (ENSEMBLE), and a recent method (MemLoci) that specifically discriminates the localization of eukaryotic membrane proteins in: 'cell membrane', 'internal membranes', 'organelle membranes'. MemLoci scores with accuracy of 70% and generalized correlation coefficient (GCC) of 0.50 on a rigorous homology-unbiased validation set and overpasses other predictors for subcellular localization. The annotation process is based both on inheritance through homology and computational methods. Each submitted protein first retrieves, when available, up to 25 similar proteins (with sequence identity ≥50% and alignment coverage ≥50% on both sequences). This helps the identification of membrane-associated proteins and detailed localization tags. Each protein is also filtered for the presence of a GPI anchor [0.8% false positive rate (FPR)]. A positive score of GPI anchor prediction labels the sequence as exposed to 'Cell surface'. Concomitantly the sequence is analysed for the presence of a signal peptide and classified with MemLoci into one of three discriminated classes. Finally the sequence is filtered for predicting its putative all-alpha protein membrane topology (FPR <1%). The web server is available at: http://mu2py.biocomp.unibo.it/mempype.

MemLoci: predicting subcellular localization of membrane proteins in eukaryotes.

MOTIVATION: Subcellular localization is a key feature in the process of functional annotation of both globular and membrane proteins. In the absence of experimental data, protein localization is inferred on the basis of annotation transfer upon sequence similarity search. However, predictive tools are necessary when the localization of homologs is not known. This is so particularly for membrane proteins. Furthermore, most of the available predictors of subcellular localization are specifically trained on globular proteins and poorly perform on membrane proteins. RESULTS: Here we develop MemLoci, a new support vector machine-based tool that discriminates three membrane protein localizations: plasma, internal and organelle membrane. When tested on an independent set, MemLoci outperforms existing methods, reaching an overall accuracy of 70% on predicting the location in the three membrane types, with a generalized correlation coefficient as high as 0.50. AVAILABILITY: The MemLoci server is freely available on the web at: http://mu2py.biocomp.unibo.it/memloci. Datasets described in the article can be downloaded at the same site.

Sequence-based feature prediction and annotation of proteins.

A recent trend in computational methods for annotation of protein function is that many prediction tools are combined in complex workflows and pipelines to facilitate the analysis of feature combinations, for example, the entire repertoire of kinase-binding motifs in the human proteome.

PredGPI: a GPI-anchor predictor.

BACKGROUND: Several eukaryotic proteins associated to the extracellular leaflet of the plasma membrane carry a Glycosylphosphatidylinositol (GPI) anchor, which is linked to the C-terminal residue after a proteolytic cleavage occurring at the so called omega-site. Computational methods were developed to discriminate proteins that undergo this post-translational modification starting from their aminoacidic sequences. However more accurate methods are needed for a reliable annotation of whole proteomes. RESULTS: Here we present PredGPI, a prediction method that, by coupling a Hidden Markov Model (HMM) and a Support Vector Machine (SVM), is able to efficiently predict both the presence of the GPI-anchor and the position of the omega-site. PredGPI is trained on a non-redundant dataset of experimentally characterized GPI-anchored proteins whose annotation was carefully checked in the literature. CONCLUSION: PredGPI outperforms all the other previously described methods and is able to correctly replicate the results of previously published high-throughput experiments. PredGPI reaches a lower rate of false positive predictions with respect to other available methods and it is therefore a costless, rapid and accurate method for screening whole proteomes.

The prediction of protein subcellular localization from sequence: a shortcut to functional genome annotation.

Automated sequence annotation is a major goal of post-genomic era with hundreds of genomes in the databases, from both prokaryotes and eukaryotes. While the number of fully sequenced chromosomes from microbial organisms exponentially increased in the last decade above 600, presently we know the whole DNA content of only 25 eukaryotic organisms, including Homo sapiens. However, the process of genome annotation is far from being completed. This is particularly relevant in eukaryotes, whose cells contain several subcellular compartments, or organelles, enclosed by membranes, where different relevant functions are performed. Translocation across the membrane into the organelles is a highly regulated and complex cellular process. Indeed different proteins and/or protein isoforms, originated from genes by alternative splicing, may be conveyed to different cell compartments, depending on their specific role in the cell. During recent years the prediction of subcellular localization (SL) by computational means has been an active research area. Several methods are presently available based on different notions and addressing different aspects of SL. This review provides a short overview of the most well performing methods described in the literature, highlighting their predictive capabilities and different applications.

BaCelLo: a balanced subcellular localization predictor.

MOTIVATION: The knowledge of the subcellular localization of a protein is fundamental for elucidating its function. It is difficult to determine the subcellular location for eukaryotic cells with experimental high-throughput procedures. Computational procedures are then needed for annotating the subcellular location of proteins in large scale genomic projects. RESULTS: BaCelLo is a predictor for five classes of subcellular localization (secretory pathway, cytoplasm, nucleus, mitochondrion and chloroplast) and it is based on different SVMs organized in a decision tree. The system exploits the information derived from the residue sequence and from the evolutionary information contained in alignment profiles. It analyzes the whole sequence composition and the compositions of both the N- and C-termini. The training set is curated in order to avoid redundancy. For the first time a balancing procedure is introduced in order to mitigate the effect of biased training sets. Three kingdom-specific predictors are implemented: for animals, plants and fungi, respectively. When distributing the proteins from animals and fungi into four classes, accuracy of BaCelLo reach 74% and 76%, respectively; a score of 67% is obtained when proteins from plants are distributed into five classes. BaCelLo outperforms the other presently available methods for the same task and gives more balanced accuracy and coverage values for each class. We also predict the subcellular localization of five whole proteomes, Homo sapiens, Mus musculus, Caenorhabditis elegans, Saccharomyces cerevisiae and Arabidopsis thaliana, comparing the protein content in each different compartment. AVAILABILITY: BaCelLo can be accessed at http://www.biocomp.unibo.it/bacello/.