Archive for the ‘network’ tag
I came across Takigawa et al where they address polypharmacology by investigating drug-target pairs. Their approach is to simultaneously identify substructures from the ligand and subsequences from the target and combine this information to suggest drug-target pairs that represent some form of polypharmacology. More specifically their hypothesis is that “polypharmacological principles” are embedded in a special set of paired fragments (substructures on the ligand side, subsequence on the target side). When you think about it, this is a more generalized (abstract?) version of a pharmacophore that makes the role of the target explicit.
Their approach originates from two assumptions
These results suggest that targets of promiscuous drugs can be dissimilar, implying that only a small part of each target is related with the principle of polypharmacology.
Similarly, recent research shows that smaller drugs in molecular weight are likely to be more promiscuous, suggesting that small fragments in each ligand would be a key to drug promiscuity
These lead to their hypothesis
… that paired fragments significantly shared in drug-target pairs could be crucial factors behind polypharmacology.
Based on this idea they first apply a frequent itemset algorithm to identify pairs of subgraph (SG) and subsequences (SS), that occur frequently (more than 5%) in the drug-target pairs. After identifying about 10,000 such SS-SG pairs, they define a sparse fingerprint, where each bit corresponds to one such pair. Using these fingerprints they then cluster the drug-target pairs, ending up with a selection of clusters. They then propose that individual clusters represent distinct polypharmacologies.
Our significant substructure pairs partitioned drug-target pairs covering most of approved drugs into clusters, which were clearly separated from each other, implying that each cluster corresponds to a unique polypharmacology type
While the underlying algorithms to obtain their results are nice, a lot of things weren’t clear.
Foremost, given the above quote, it’s not exactly clear from the paper what is meant by “unique polypharmacology type“? Given that a cluster will consist of multiple drugs and multiple targets, it is not apparent from the text that a cluster highlights either promiscuity of compounds or ligand preferences for a small number of targets. While I think this is a major issue there are some other lesser problems
- I get the impression that they consider promiscuity and polypharmacology as equivalent concepts. While there is a degree of similarity, I’d regard polypharmacology more as a rationally, controlled type of promiscuity
- Most fragments they highlight in Figure 2 are relatively trivial paths. Certainly, reactive groups can lead to promiscuity; none of the subgraphs list exhibit reactive functionality and their application of the frequent itemset method, using a support of 5% could easily filter these out
- Given they consider arbitrary subsequences of the target, the resulting associations could be meaningless. Again, it’d be interesting to note, in cases where crystal structure is available, how many of the subsequences, in the list of significant SS-SG pairs, lie in or around the binding site. A related question would be, of the SG-SS pairs associated with a cluster, how are individual subsequences distributed? Few unique subsequences could point towards a common binding site or active domain.
- Related to the previous point, it’d be interesting to see in how many of the SG-SS paired fragments, the members correspond to actual interacting motifs (again based on crystal structure data).
- One could argue that just using string subsequences to characterize the target misses information on important ligand-target interactions.
And while they may be the first to consider an analysis of drug-target pairs specifically, the idea of considering ligand and target simultaneously is not new. For example, the SiFT approach is quite similar and was described in 2004.
So, even though the paper seems pretty fuzzy on the supposed polypharmacology that they identify, it is overall an interesting paper (and one of the more interesting cheminformatics applications of frequent itemset methods).
Today while working on a project I needed to get access to the Gene Ontology hierarchy. While there a number of GO browsers such as Amigo, I needed access to the raw data to generate a graph that I could then slice and dice. A few minutes with Python led to a simple solution.
The program parses the OBO 1.2 formatted GO data file (either by directly downloading it or from a local file) and outputs a flat dictionary listing the term ID’s, names, namespace etc and a network representation of the GO hierarchy in ncol format. It uses a simple (and relatively non-robust) class to represent the data as an undirected graph (not really correct), though it’d be easy to use something like igraph to start doing some real network analysis. It’s certainly not a comprehensive solution, but I thought I’d put it out there.
I’ve been working for some time with the PubChem Bioassay collection – a set of 1293 assays that cover a range of techniques (enzymatic, phenotypic etc.), targets and sizes (from 20 molecules to 200,000 molecules). In addition, some assays are primary, high-throughput assays whereas a number of them are smaller, confirmatory assays. While an extremely valuable collection, one of the drawbacks is the lack of curation. This has led to some people saying that the data is too noisy to be useful. Yes, the noise is a problem, but I think there’s still useful data to extract and model.
One of the problems that I have faced is that while one can perform a full text search for assays on PubChem, there is no form of annotations on the assays themselves. One effect of this is that it is difficult to link an assay to other biological resources (though for enzymatic assays, one can determine a Pubmed protein identifier). While working on my bioassay network project, I needed annotations and I didn’t want to do it manually.
Last year, John Van Drie and I published two papers (here and here) on the Structure Activity Landscape Index, (SALI) which is a way to view SAR data as a network of compounds. Along with the paper ,I put up a simple Java application (licensed under the LGPL) to generate and explore these networks. – you only need to provide a file containing SMILES and activities. It’s based on ZGRViewer – a very slick GUI for Graphviz generated networks. I finally got around to reorganizing the code and putting it up on a GitHub repository. You can get more details of the application and the last stable version here.
The Curious Wavefunction has a nice post on the issue of selective and non-selective kinase inhibitors. An interesting commentary, especially in the light of the recent paper on network polypharmcology. While there have been a number of papers on polypharmcology and the idea itself is very attractive, it has seemed to me that for this approach to succeed we need very detailed information on the targets and systems involved in these networks. Indeed, a current project of mine is currently hitting this problem. As Ashutosh notes,
… in the first place we don’t even know what specific subset of kinases to hit for treating a particular disease. First comes target validation, then modulation.