GtoImmuPdb: technical update March 2017

The 4th alpha-release (v4.0) of the Guide to IMMUNOPHARMACOLOGY was released on 23rd March 2017. This blog post summarises some of the main features of the release and other developments as we moved toward our first public, beta-release in Spring 2017

An early synopsis of the project can be found in this blog post. You can also review our previous technical blogs on GtoImmPdb.

Development Progress

Alpha-Release v4.0 Portal

The disease panel on the portal is now active (Fig. 1). This contains two links, which link to different views of the Immuno Disease List page.

portal_v4

Fig 1. GtoImmuPdb v4.0 portal.
New disease panel (lower left-hand side) is now functional. New disease menu item is also included

The Immuno Disease List pages provide an overview of the disease-target and disease-ligand associations, curated in the database specifically for GtoImmuPdb

Alpha-Release v4.0 Navigation

In conjunction with adding the Immuno Disease List pages we have extended the menu-bar navigation to contain a Disease menu. This holds two sub-menu items, one points to the disease list associations to targets and one to the disease list associations to ligands.

Disease List Page

The disease list page is designed to display all disease associations curated as part of the GtoImmuPdb. One single page, it is divided into two views, one showing disease associations to targets in the database and the other showing disease associations to ligands (Fig. 2).

disease_list

Fig. 2. Immuno Disease List page. Showing target to disease associations.

Users can switch between the two views (Targets or Ligands) using a tab at the top of the page.

The format of the list of disease associations is similar for both targets and ligands. Both show one section or row per disease. Along with the disease name any external references to other disease resources (OMIM, Disease Ontology and Orphanet) are shown.

Next to each disease name are the total number of either targets or ligands associated in GtoImmuPdb to that disease. By default, the full details of the target or ligand associations are hidden. These can be displayed by clicking the ‘display all ….’ link.

At the top of the page are two toggle buttons that can be used to show or hide all the associations for all disease, if users so wish.

For targets, when the associations are displayed they show the name of the target and curated comments about the association. It also lists any ligands for which that target is a primary target and highlights if the ligand is an approved drug.

For ligands, it shows the name of the ligand, comments and any literature references for the association.

Immuno-Relevance Searching

The ranking of search results has been developed to apply a weighting to targets and ligands that are returned from a search that are consider of greater immunological relevance. The weighting is only applied when searching from a GtoImmuPdb page, not from the standard GtoPdb pages.

The criteria used to determine the immune-relevancy (and it’s weighting) of a given target or ligand is based on the amount of immunological data curated against it. For example, targets that have process, cell type and disease data annotated against them will rank higher than targets with only process data. The weighting is applied in addition to existing search weightings – so exact matches (to target or ligand name for example) will still score highest. We will be refining this relevancy scoring during testing, both in alpha and beta releases.

Cell Type Associations – Definitions

We have extended the submission tool and database to better capture and store definitions of cell type categories. This enable reference and ligands to be tagged in the definition text. We have also further developed the cell type list page to display these definitions at the top of the table (Fig. 3) – with the ability to toggle their display.

celltype_list_page

Fig. 3. Cell type list view – show toggle-able definitions

http://ow.ly/PakB30amkZQ

This project is supported by a 3-year grant awarded to Professor Jamie Davies at the University of Edinburgh by the Wellcome Trust (WT).

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Posted in Guide to Immunopharmacology, Technical

Hot topics: Social network architecture of human immune cells unveiled by quantitative proteomics

The authors applied high-resolution mass-spectrometry-based proteomics to characterize 28 primary human hematopoietic cell populations in steady and activated states at a depth of >10,000 proteins in total. The protein copy numbers revealed a specialization of immune cells for ligand and receptor expression, thereby connecting distinct immune functions. They discuss the fundamental intercellular communication structures and previously unknown connections between cell types. The publicly accessible (http://www.immprot.org/) proteomic resource provides a framework for the orchestration of cellular interplay and a reference for altered communication associated with pathology.

[1] Rieckmann et al. (2017). Nat. Immunol. doi: 10.1038/ni.3693. [Epub ahead of print]. Social network architecture of human immune cells unveiled by quantitative proteomics. [PMID 28263321].

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Hot topics: Functional Selectivity in Cytokine Signaling Revealed Through a Pathogenic EPO Mutation.

Abstract: Cytokines are classically thought to stimulate downstream signaling pathways through monotonic activation of receptors. We describe a severe anemia resulting from a homozygous mutation (R150Q) in the cytokine erythropoietin (EPO). Surprisingly, the EPO R150Q mutant shows only a mild reduction in affinity for its receptor but has altered binding kinetics. The EPO mutant is less effective at stimulating erythroid cell proliferation and differentiation, even at maximally potent concentrations. While the EPO mutant can stimulate effectors such as STAT5 to a similar extent as the wild-type ligand, there is reduced JAK2-mediated phosphorylation of select downstream targets. This impairment in downstream signaling mechanistically arises from altered receptor dimerization dynamics due to extracellular binding changes. These results demonstrate how variation in a single cytokine can lead to biased downstream signaling and can thereby cause human disease. Moreover, we have defined a distinct treatable form of anemia through mutation identification and functional studies.

[1] Kim et al. (2017). Cell 168(6):1053-1064. Functional Selectivity in Cytokine Signaling Revealed Through a Pathogenic EPO Mutation. [PMID 28283061].

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Hot topics: An atlas of human long non-coding RNAs with accurate 5′ ends

Using FANTOM5 cap analysis of gene expression (CAGE) data, the authors integrate multiple transcript collections to generate a comprehensive atlas of 27,919 human lncRNA genes with high-confidence 5′ ends and expression profiles across 1,829 samples from the major human primary cell types and tissues. The authors combine their findings to identify 19,175 potentially functional lncRNAs in the human genome.

[1] Hon et al. (2017). Nature 543(7744):199-204. An atlas of human long non-coding RNAs with accurate 5′ ends. [PMID 28241135].

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Hot topics: The Ecstacy and Agony of Assay Interference Compounds

Editorial article in Biochemistry.

[1] Alrich et al. (2017). Biochemistry 56(10):1363-1366. The Ecstacy and Agony of Assay Interference Compounds. [PMID: 28244742]

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Hot topics: Ligand and Target Discovery by Fragment-Based Screening in Human Cells

Description of a platform that marries fragment-based ligand discovery with quantitative chemical proteomics to map thousands of reversible small molecule-protein interactions directly in human cells, many of which can be site-specifically determined.

[1] Parker et al. (2017).Cell. 168(3):527-541. Ligand and Target Discovery by Fragment-Based Screening in Human Cells. [Cell article]

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Database release 2017.2

Our 2nd database release of 2017 was published on 23rd March 2017. It now includes 15019 interactions between 2809 targets and 8832 ligands. For full release statistics see the About the Guide to PHARMACOLOGY page.

Target updates

The major part of the work to update the target family summary pages has been completed in advance of producing the Concise Guide to PHARMACOLOGY 2017/18 from the database, which is due out later this year. For the next version, we have been working towards trying to make the information more concise, and limiting both ligands and further reading to the 5 most useful in many cases. Obviously there are some targets where it makes sense to have more or less than 5 displayed on the summary page, but in any case, all the ligands can still be viewed on the detailed target page, and the website contains more further reading references than are included in the published Concise Guide. We are very grateful to all the contributors and the editors who have provided information.

Since most of our curation effort has gone into these updates, the only GPCR detailed page updates this time are the Gonadotrophin-releasing hormone receptors.

Ligand updates

We have refreshed our PDB ligand links and now have 1283 links from ligands to individual RCSB PDB ligand pages and the crystal structures they are found in, e.g. LSD recently crystalised with 5-HT2B.

Meanwhile, our development team has prepared the following new website features and updates:

Web services updates

The REST web services have been updated and now include interactions web services  providing lists of target-ligand pairs which can be filtered by target/ligand type and properties, binding affinity etc., and references web services which can retrieve references by id or the full interaction reference set.

Graphs comparing ligand activity across species

We have developed new ligand activity graphs comparing activity ranges across species using data extracted from GtoPdb and ChEMBL. These are available via the ‘biological activity’ tab (screenshot 1) on ligand pages but currently only for ligands that are also in ChEMBL. For example, DPCPX (screenshot 2) shows similar activity at A1 receptors across a range of species tested.

DCPCX_ligand_page

Screenshot 1. New link to view charts of activity data on the DPCPX ligand page biological activity tab

DPCPX

Screenshot 2. Chart showing DPCPX ligand activity data from ChEMBL and GtoPdb across 4 species. Mouse-over a plot to see the median, lower and upper quartiles, and minimum and maximum data points for each activity type.

Mouse-over a plot to see the median, interquartile range, low and high data points. A value of zero indicates that no data are available. A separate chart is created for each target, and where possible the algorithm tries to merge ChEMBL and GtoPdb targets by matching them on name and UniProt accession, for each available species. However, please note that inconsistency in naming of targets may lead to data for the same target being reported across multiple charts.

At the end of the page, below the charts, is a table listing all the data points that were used to build the charts, the source databases, assay details, and links to the original references and PubMed.

The graphs can be useful for comparing data across species when choosing model organisms to use for experiments. For example, the ligand palosuran is known to have 100-fold lower binding inhibitory potency on rat versus human UT receptor (screenshot 3).

Palosuran

Screenshot 3. Palosuran activity at human and rat UT receptors.

Extracting ChEMBL activities

Since ChEMBL contains an enormous amount of data (>14.3 million activities in ChEMBL 22) we have filtered and extracted the most useful data and tried to standardise them to the terms used in GtoPdb. Data are selected according to the following criteria:

  1. The target must have a type of ‘SINGLE PROTEIN’, ‘PROTEIN COMPLEX’, or ‘PROTEIN COMPLEX GROUP’
  2. Affinity types are combined and normalised as follows:
    Kd = Dissociation constant, Kd, K app, K Bind, K calc, Kd’, KD app, KD’, Kd(app), KD50, Kdiss, Relative Kd, Binding constant, K aff, K diss, KD/Ki
    pKd = -Log Kdiss, -Log KD50, pKd, pKD, logKd, -Log Kd, Log Kd, -Log KD, Log KD
    Ki = Adjusted Ki, Ki, ki, Ki app (inact), Ki app, Ki(app), Ki_app, Ki’, Ki”, KI’, K’i, Kiact, Ki high, Ki low, KiH, KiL, Kii, KII, Kic, Ki.c, Ki comp, Ki’ uncomp
    pKi = pKi(app), pKi, -Log K0.5, Log Ki, logKi, -Log Ki, pKiH, pKiL
    IC50 = IC50 app, IC50, IC50 max, I50, Mean IC50, IC50H, IC50L
    pIC50 = pIC50, pIC50(app), -Log I50, logIC50, log IC50, Log IC50, -Log IC50, pI50, pIC50(calc)
    EC50 = EC50
    pEC50 = pEC50 diss, pEC50, -Log EC50, Log EC50, logEC50
    A2 = A2
    pA2 = pA2, pA2(app), pA2 app, pA2/pKB
  3. Raw data (e.g. Kis are converted into their negative log to base 10 values (e.g. pKis)
  4. Activities deemed by ChEMBL curators to be “outside typical range” are ignored (to prevent skew)
  5. Only binding (‘B’) and functional (‘F’) assays are included (no large-scale screening data)

We have tried to be as inclusive as possible with the ChEMBL data, but please note that due to the sheer volume, there will be data that have not yet been manually checked by the ChEMBL curators and we always ask users to refer back to the original references when using the data.

We hope this new feature will be useful to our users, and we welcome any feedback you may have.

Posted in Concise Guide to Pharmacology, Database updates, Technical