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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.

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.

Reasoning over paths in large scale knowledge graphs is an important problem for many applications. In this paper we discuss a simple approach to automatically build and rank paths between a source and target entity pair with learned embeddings using a knowledge base completion model (KBC). We assembled a knowledge graph by mining the available biomedical scientific literature and extracted a set of high frequency paths to use for validation. We demonstrate that our method is able to effectively rank a list of known paths between a pair of entities and also come up with plausible paths that are not present in the knowledge graph. For a given entity pair we are able to reconstruct the highest ranking path 60% of the time within the the top 10 ranked paths and achieve 49% mean average precision. Our approach is compositional since any KBC model that can produce vector representations of entities can be used., Submitted at the TextGraphs2019 Workshop at EMNLP 2019 Conference

We present a novel framework to deal with relation extraction tasks in cases where there is complete lack of supervision, either in the form of gold annotations, or relations from a knowledge base. Our approach leverages syntactic parsing and pre-trained word embeddings to extract few but precise relations,which are then used to annotate a larger cor-pus, in a manner identical to distant supervision. The resulting data set is employed to fine tune a pre-trained BERT model in order to perform relation extraction. Empirical evaluation on four data sets from the biomedical domain shows that our method significantly outperforms two simple baselines for unsupervised relation extraction and, even if not using any supervision at all, achieves slightly worse results than the state-of-the-art in three out of four data sets. Importantly, we show that it is possible to successfully fine tune a large pre-trained language model with noisy data, as op-posed to previous works that rely on gold data for fine tuning.

Although a scientific web application that is intuitive can help scientists utilize data more easily and advance their research, there is little guidance on how to design such an application in the academic literature. We discuss how we designed an intuitive application for bench scientists working in drug discovery following an approach that can be applied to the design and development of scientific resources in a broad range of disciplines.

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.

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.

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.

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

1. Why membrane proteins? 2 Many functions 3 Bioinformatics and membrane proteins: is it feasible to predict the 3D structure of a membrane protein? 4 Predicting the topology of membrane proteins 5 How many methods to predict membrane protein topology? 5.1 From theory to practice 6 Benchmarking the predictors of transmembrane topology 6.1 Testing on membrane proteins of known structure and topology 6.2 Topological experimental data 6.3 Validation towards experimental data 7 How many membrane proteins in the Human genome? 8 Membrane proteins and genetic diseases: PhD-SNP at work 9 Last but not least: 3D MODELLING of membrane proteins 10 What can currently be done in practice? 11 Can we improve?

Compartmentalization plays a major role in eukaryotic cells by making possible the fine regulation of complex biochemical pathways. Each protein needs the right biochemical context to operate, therefore the knowledge of the subcellular localization of a protein is essential in order to understand its functions and its pattern of interactions in protein networks. BaCelLo is a predictor for the subcellular localization of eukaryotic proteins and it is based on several Support Vector Machines (SVMs) arranged in a decision tree (Fig 1). Starting from the residue sequence, BaCelLo discriminates five different localizations: secretory pathway, cytoplasm, nucleus, mitochondrion and chloroplast. The predictor analyzes the protein residue sequence and its evolutionary profile considering information from the whole sequence and from its N- and C-terminal regions. Three different predictors are available for three different eukaryotic kingdoms: Metazoa, Viridiplantae and Fungi. The distinctive features of BaCelLo are: 1. a homology-reduced dataset for training and testing the predictor, in order to avoid redundancy. This dataset was compiled starting from the Swissprot data base (release 48) and contains proteins whose subcellular localization was experimentally annotated. The dataset was reduced by similarity so that no protein in the dataset share more than 30% identity; 2. the implementation of three kingdom-specific predictors to take into account differences in subcellular localization mechanisms; 3. the evolutionary profile to extract evolutionary information from the residue sequence. 4. a hierarchic tree for the predictions; 5. the introduction of a unique balancing procedure in SVMs that corrects the biases between the different classes due to the disproportions in the training set . BaCelLo proved to outperform all the other state-of-art methods publicly available, when validated on a set of protein sequences independent of the training set.

MOTIVATIONS: Subcellular localization is a key feature for functional characterization of proteins. Experimental determination is an expensive and time consuming procedure, that up to now, has been achieved only for a small subset of the known proteins. Large-scale experiments have been carried out for determining the subcellular location of all the proteins of an organism. Limitations in the experimental procedure allow, to date, to analyse only simple, unicellular species, such as Saccharomyces cerevisiae (Huh et al., 2003), and cannot be easily scaled up to more complex ones. In this poster we present eSLDB, a database of protein subcellular localization, which aims to compensate this gap providing an annotation for the entire proteomes of eukaryotic organisms. The database contains the experimental localizations, when available, the homology-based annotations, when feasible, and predictions performed with machine learning based methods. Up to date we processed five proteomes: Homo sapiens, Mus musculus, Caenorhabditis elegans, Saccharomyces cerevisiae and Arabidospis thaliana. RESULTS: The SwissProt annotations for subcellular localization of eukaryotes can be grouped into 16 major classes: Nucleus, Cytoplasm, Mitochondrion, Plastid, Golgi, Endoplasmic reticulum, Lysosome, Endosome, Vescicles, Peroxisome, Vacuole, Cell wall, Secretory pathway, Extracellular, Cytoskeleton and Membrane. Only 9% of all the SwissProt entries for the five species taken into account contains a record reporting the experimental subcellular localization. The rate of experimental annotation ranges from 27% of S. cerevisiae proteome to less than 3% for A. thaliana and C. elegans. In these cases eSLDB contains the annotation extracted from the SwissProt database using an automated tool that parses the SUBCELLULAR LOCALIZATION section of the COMMENT field. The words directly and/or implicitly referring to one of 16 classes are taken into account. Entries annotated as “probable”, “possible” or “by similarity” were not considered as experimental annotations. All the proteins are then assigned using both sequence similarity and state-of-art predictors. Based on the fact that proteins sharing high sequence identity usually have the same subcellular localization (Pierleoni et al, 2006), we aligned each sequence with the experimentally annotated proteins belonging to the same kingdom (metazoa, fungi or viridiplantae). The annotation of the best scoring match having an E-value lower than 10-4, if existing, is then transferred to the query sequence. This procedure assigns localization to 45% of the proteins in the database. This rate ranges from 20% of C. elegans up to 62% for H. sapiens. In order to annotate the rest of the proteins, that comprise the largest fraction of the proteome, a pipeline of predictors based on machine learning is needed. We used Spep (Fariselli et al, 2003) and ENSEMBLE (Martelli et al, 2003) for discriminating membrane proteins, then BaCelLo (Pierleoni et al, 2006) for assigning localization for soluble proteins. These are among the best available methods. To achieve a good reliability, the 16 original classes are reduced to 6 macro-classes: Membrane, Nucleus, Cytoplasm, Secretory pathway, Mitochondrion and Plastid. The decision tree structure of the prediction is also reported, deriving from the original output of the used methods. All the data are available at our website and fully searchable either by sequence or protein name (ENSEMBL or TAIR) submission. CONCLUSIONS: eSLDB is the only available database containing annotations for subcellular localizations of whole eukaryotic proteomes, that includes experimental data, homology-based annotations and predictions. Other available eukaryotic proteomes are currently under process and will be added to the database. eSLDB is publicly available at http://gpcr.biocomp.unibo.it/esldb/.

Cancer biomarker discovery is an extremely active research field in both academia and pharmaceutical companies. Here we present a group of candidate tumor markers identified by a high-throughput immunohistochemistry approach based on the use of a library of 1600 mouse antibodies raised against marginally-characterized human proteins. Tissue microarray (TMA) representing breast, lung colon ovary and prostate cancers were used to identify novel tumor markers. Eighty-nine proteins were found over-expressed in one or more of the five tumors under analysis. The validation process is still in progress and so far has been undertaken on twenty-six of the eighty-nine hits. They are confirmed over-expressed in TMAs carrying 50 tumor samples for each tumor type and, in particular, nineteen proteins were found expressed in one or more tumors with frequencies ranging from 20% to 96%. Interestingly, some of the proteins are simultaneously over-expressed in specific patients' groups, thus opening the possibility of tissue diagnostic/prognostic/predictive applications based on combinatorial markers. Moreover, in vitro characterization studies indicated that some markers play a pivotal role in tumor-related cell processes like cell proliferation (EXN32; EXN36; EXN7), invasiveness (EXN32; EXN36; EXN7; EXN4), clonal growth (EXN1) and pro-angiogenesis (EXN11). A panel of monoclonal antibodies highly specific the biomarkers is already available and proved to specifically detect the markers in tumor cells derived from surgical resections. The newly identified candidate markers are new tools for the development of specific drug therapies and diagnostic products. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2011 Nov 12-16; San Francisco, CA. Philadelphia (PA): AACR; Mol Cancer Ther 2011;10(11 Suppl):Abstract nr B180.

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 genome 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. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2011 Nov 12-16; San Francisco, CA. Philadelphia (PA): AACR; Mol Cancer Ther 2011;10(11 Suppl):Abstract nr C96.

Cancer biomarker discovery is an extremely active research track in both academia and pharma industries. Several high throughput technologies were applied through the years to identify proteins specifically related to cancerous phenotype. However, most putative biomarkers identified from conventional proteomic/transcriptomic strategies still need to be clinically validated. Here we present a cancer biomarker discovery approach based on the use of a large library of antibodies raised against recombinant human proteins to detect tumor-associated proteins by immune-histochemistry analysis of clinical tumor tissues. Starting from the whole human genome, genes encoding proteins predicted as membrane- or secreted were selected and high through-put cloned and expressed. Recombinant proteins were used to build a polyclonal antibody library (YOMICS®) currently comprising more than 1700 murine immune sera. The ability of sera to recognize specific targets predominantly present in tumors was assessed by Tissue MicroArray (TMA, a miniaturized immunohistochemistry analysis) of tumor tissue samples and healthy controls from pedigreed patients affected by the most common human tumor types, including colon, ovary, breast, lung, ovary and prostate cancers. While the screening is still in progress, six antibodies were identified showing high reactivity on a high percentage (ranging from 40 to 95%) of tumor tissues on one or more cancer type, with negative staining on normal tissues. The corresponding protein targets, being novel tumor-associated proteins, were validated and characterized at cellular and molecular level by analyzing their expression, cellular localization and biological role in a panel of tumor cell lines. Interestingly, two of the six proteins are localized at the plasma membrane, while the other proteins are intracellular. Data so far available indicate that one of the six proteins confers an invasive phenotype to tumor cell lines. The newly identified proteins are promising candidates as new biomarkers and could be exploited to develop target-specific drug therapies. Note: This abstract was not presented at the AACR 101st Annual Meeting 2010 because the presenter was unable to attend. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 5579.

The combination of prediction tools in complex workflows and pipelines facilitates prediction of protein features from sequence., 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.