Skip to main content

Paper: Chemical, Target, and Bioactive Properties of Allosteric Modulation


We have just had a paper accepted in PLoS Computational Biology on the work we've done on allosteric modulators (first mentioned on the blog here).  The work is based on the mining of allosteric bioactivity points from ChEMBL_14. The data set of allosteric and non-allosteric interactions is available on our FTP site (here). This blogpost will just highlight some sections of the paper, but we would like to refer the interested reader to the full paper (here). 

Dataset
The dataset contains ChEMBL annotated and cleaned data divided in both an 'allosteric' set and a 'non-allosteric' (or background) set. Abstracts and titles mentioning allosteric keywords were pulled and from the resulting papers we extracted the primary target and all bioactivities on this primary target. From the remainder of the papers we also retrieved the primary target and all bioactivities on this primary target in a similar manner. 

Targets
When we observed the target distribution in both sets, we saw differences (see below ; also touched upon in the previous post). Targets that are known to be amenable to allosteric modulation are indeed well represented in our allosteric set (e.g. Class C GPCRs). However there are also some interesting observations that we did not expect (please see the paper for further details). 



Chemistry
Obviously, as we are the ChEMBL group, we are interested in potential chemical differences between the allosteric and background set. Interestingly, the allosteric modulators appear to form a subset of the background set, rather than that they are distinct from the background set. We have calculated a large number of descriptors and compared the sets (median values, but also histograms; all available on the FTP). We observe that allosteric modulator molecules tend to be smaller, more lipophilic and more rigid. Although there is understandably a large variance over the diverse targets included in the set. Shown here is the rigidity index calculated over the full sets (L0), but when the target selection becomes more concise, the differences become more distinct.



Bioactivity
Likewise we observe differences between our allosteric subset and the background set with regard to bioactivity. While 'allosteric modulation' is a very diverse concept, in which the specific manner wherein the protein is influenced by the small molecule differs per protein - ligand pair, we do observe some general differences. From our data it appears that allosteric modulators bind with a lower affinity (on average) but similar ligand efficiency (on average) when compared to our background set. In the paper we provide a more extensive discussion on this observation and we would again refer the reader given the limited space here.

Classification models
Built on the dataset we have created allosteric classifier models that can predict if an interaction is likely allosteric or not. We have tried this on the full dataset, but also on lower levels (restricting the data to e.g. Class A GPCRs). We find that we can train predictive models that gain in quality if we have a more concise dataset (eliminating some of the inter-target variation). In the paper we provide case studies on HIV Reverse Transcriptase, the adenosine receptors (family), and protein Kinase B. Here the model performance for class A GPCRs (full L2 tgt class) is shown. Note that rigidity, number of sp3 carbons, Polar Solvent Accessible Surface (normalized), and rotatable bonds fraction are most important for model fit.


All data is ChEMBL and hence can be freely downloaded and used. Please let us know if you find any errors or misclassifications as we will correct them (crowd curation).

Anna, jpo, and Gerard

%T Chemical, Target, and Bioactive Properties of Allosteric Modulation
%A G.J.P. van Westen
%A A. Gaulton
%A J.P. Overington
%J PLoS. Comput. Biol.
%D 2014
%V 10
%O doi:10.1371/journal.pcbi.1003559

Comments

Popular posts from this blog

UniChem 2.0

UniChem new beta interface and web services We are excited to announce that our UniChem beta site will become the default one on the 11th of May. The new system will allow us to better maintain UniChem and to bring new functionality in a more sustainable way. The current interface and web services will still be reachable for a period of time at https://www.ebi.ac.uk/unichem/legacy . In addition to it, the most popular legacy REST endpoints will also remain implemented in the new web services: https://www.ebi.ac.uk/unichem/api/docs#/Legacy Some downtime is expected during the swap.  What's new? UniChem’s current API and web application is implemented with a framework version that’s not maintained and the cost of updating it surpasses the cost of rebuilding it. In order to improve stability, security, and support the implementation and fast delivery of new features, we have decided to revamp our user-facing systems using the latest version of widely used and maintained frameworks, i

A python client for accessing ChEMBL web services

Motivation The CheMBL Web Services provide simple reliable programmatic access to the data stored in ChEMBL database. RESTful API approaches are quite easy to master in most languages but still require writing a few lines of code. Additionally, it can be a challenging task to write a nontrivial application using REST without any examples. These factors were the motivation for us to write a small client library for accessing web services from Python. Why Python? We choose this language because Python has become extremely popular (and still growing in use) in scientific applications; there are several Open Source chemical toolkits available in this language, and so the wealth of ChEMBL resources and functionality of those toolkits can be easily combined. Moreover, Python is a very web-friendly language and we wanted to show how easy complex resource acquisition can be expressed in Python. Reinventing the wheel? There are already some libraries providing access to ChEMBL d

LSH-based similarity search in MongoDB is faster than postgres cartridge.

TL;DR: In his excellent blog post , Matt Swain described the implementation of compound similarity searches in MongoDB . Unfortunately, Matt's approach had suboptimal ( polynomial ) time complexity with respect to decreasing similarity thresholds, which renders unsuitable for production environments. In this article, we improve on the method by enhancing it with Locality Sensitive Hashing algorithm, which significantly reduces query time and outperforms RDKit PostgreSQL cartridge . myChEMBL 21 - NoSQL edition    Given that NoSQL technologies applied to computational chemistry and cheminformatics are gaining traction and popularity, we decided to include a taster in future myChEMBL releases. Two especially appealing technologies are Neo4j and MongoDB . The former is a graph database and the latter is a BSON document storage. We would like to provide IPython notebook -based tutorials explaining how to use this software to deal with common cheminformatics p

ChEMBL 30 released

  We are pleased to announce the release of ChEMBL 30. This version of the database, prepared on 22/02/2022 contains: 2,786,911 compound records 2,157,379 compounds (of which 2,136,187 have mol files) 19,286,751 activities 1,458,215 assays 14,855 targets 84,092 documents Data can be downloaded from the ChEMBL FTP site: https://ftp.ebi.ac.uk/pub/databases/chembl/ChEMBLdb/releases/chembl_30/ Please see ChEMBL_30 release notes for full details of all changes in this release:  https://ftp.ebi.ac.uk/pub/databases/chembl/ChEMBLdb/releases/chembl_30/chembl_30_release_notes.txt New Deposited Datasets EUbOPEN Chemogenomic Library (src_id = 55, ChEMBL Document ID CHEMBL4689842):   The EUbOPEN consortium is an Innovative Medicines Initiative (IMI) funded project to enable and unlock biology in the open. The aims of the project are to assemble an open access chemogenomic library comprising about 5,000 well annotated compounds covering roughly 1,000 different proteins, to synthesize at least

Multi-task neural network on ChEMBL with PyTorch 1.0 and RDKit

  Update: KNIME protocol with the model available thanks to Greg Landrum. Update: New code to train the model and ONNX exported trained models available in github . The use and application of multi-task neural networks is growing rapidly in cheminformatics and drug discovery. Examples can be found in the following publications: - Deep Learning as an Opportunity in VirtualScreening - Massively Multitask Networks for Drug Discovery - Beyond the hype: deep neural networks outperform established methods using a ChEMBL bioactivity benchmark set But what is a multi-task neural network? In short, it's a kind of neural network architecture that can optimise multiple classification/regression problems at the same time while taking advantage of their shared description. This blogpost gives a great overview of their architecture. All networks in references above implement the hard parameter sharing approach. So, having a set of activities relating targets and molecules we can tra