Archive for the ‘Paper Summary’ Category
Summary: Flux Variability Analysis of Metabolic Networks in Purple Nonsulfur Bacteria
Hadicke et al published a research paper in BMC Systems Biology. The group modeled metabolic networks of purple nonsulfur bacteria (PNSB) using flux variability analysis (FVA). FVA is slightly different to flux balance analysis (FBA) in that in the former, the biological objective is in its contraints, not the value to optimize.
So FBA can be formulated as
where Z is the biological objective function, c is a vector of coefficients that define how much each reaction contribute to the objective. v is the vector of fluxes of each reaction, and it is unknown. S is a m*n matrix. It contains the stoichiometry of the metabolic networks. 
Then FVA can be formulated as
Note that now 
This paper applied FVA to the analysis of the metabolic network in photosynthetic PNSB. The authors tested their model in several conditions. This paper is particularly interesting to me because of the methods FVA. The original paper of FVA is published in 2003. (pubmed)
Drug Mode of Action and repositioning from gene expression data
Iorio, F. et al published a paper in the PNAS journal. They built a drug network out of gene expression data from small molecule screens. Using network analysis tools, they were able to group drugs into communities using network analysis. They claim that the compounds in the same community tend to have similar MoA, or act on similar biological pathways. They correctly predicted nine anticancer compounds and discovered an unreported effect for an existing drug.
There are several interesting things in their work. First, they developed a new algorithm to merge the gene expression data from different cell lines given the same compound. This aggregation algorithm uses several techniques such as Spearman’s footrule, Borda Merging Method and the Kruskal Algorithm.
Secondly, they calculated the drug similarity based on the merged list of gene ranks from the previous step based on results from Gene Set Enrichment Analysis (GSEA). This is because the list of expressions are different for two different compounds.
Thirdly, they compared the drug similarity measure with chemical similarity measures using fingerprints or electrotopological descriptors. The results are quite uncorralted with very low pearson correlation coefficient. An interesting extension of this could be the activity cliffs – very similar compounds that having completely different biological activity profile.
One limitation of this method is that you need to have a gene expression profile of a compound to be able to use it, similar to the original connectivity map approach. The other concern is that for compounds that have inconsistent effects on different cell lines. But as the authors mentioned, “when no information on the drug MoA is available a priori, the best strategy is still
to merge profiles from multiple cell lines”.
Iorio, F. et al. Discovery of drug mode of action and drug repositioning from transcriptional responses. Proceedings of the National Academy of Sciences 107, 14621 -14626 (2010).
Paper Summary: Generative Models for Chemical Structures
Generative Models for Chemical Structures, David White, Richard C. Wilson. Journal of Chemical Information and Modeling Article ASAP
An interesting paper published on JCIM. The authors created a GMM (Gaussian mixture model) based on properties of active compounds over targets, and used the model to generate more molecules that are likely to be active. Each compound is represented based on properties extracted from a graph representation of it, and PCA conducted to reduce dimensionality. Then they sample from the built GMM, and map the samples back to a molecule.
Testing this method on DUD data sets, they authors showed that molecules generated using this method are similar to the compounds in the input sets, and docking results show that the molecules are likely to be active against a target of the input molecules.
Summary: Drug Discovery Using Chemical Systems Biology: Repositioning the Safe Medicine Comtan to Treat Multi-Drug and Extensively Drug Resistant Tuberculosis
This paper is the second paper published by the Bourne and Xie group in “Drug Discovery Using Chemical Systems Biology”. It’s a very interesting topic: the authors explain how they used chemical systems biology to discover entacapone and tolcapone, commercial available drugs for the treatment of Parkinson’t disease, are good candidates for Multi-Drug and Extensively Drug Resistant Tuberculosis (MDR-TB and XDR-TB). These drugs can inhibit the enzyme InhA, which has a similar binding sites with COMT which is their primary target for treatment of the Parkinson’s disease. InhA is essential for type II fatty acid biosynthesis and the subsequent synthesis of the bacterial cell wall. It is the common target of the anti-tubercular drugs. Their discoveries are validated by in vitro and InhA kinetic assays using tablets of Comtan, whose active component is entacapone.
The authors describe their strategy as follows:
1. The binding sites of a commercially available drug is extracted or predicted from a 3D structure or model of the target protein.
2. Off-targets with similar ligand binding sites are identified across the proteome using an efficient and accurate functional site search algorithm.
3. Atomic interactions between the putative off-targets and the drug are evaluated using protein-ligand docking. Only those off-targets that do not experience serious atomic clashes with the drug are selected for further analysis.
4. The drug is further optimized to enhance its potency, selectivity and ADME properties by taking into account both the primary target and the off-targets across the genome.
In short, the authors try to find other targets of a drug in the whole human proteome, based on binding site similarity, and docking. Then they optimize the drug based on all the targets in the proteome.
The result is interesting and inspiring. Using this strategy, they found what is described earlier: entacapone and tolcapone, drugs currently in the market for Parkinson’s disease can be good candidates for treatment of TB.
The authors elaborated on details of their research. The primary target of these drugs is human catehol-O-mehyltransferase (COMT). Using the SOIPPA algorithm, developed by the same group, they detect the common binding sites among proteins. Then they docked entacapone and tolcapone into these proteins and find that InhA were highly ranked.
Interestingly, the authors pointed out that when if comparing 2D similarity, these two drugs are not similar the current known InhA inhibitors. When docking 20K drug-like compounds into InhA, entacapone ranked very low. In other words, using virtual screening, they will not be found as potential InhA inhibitors. The logP of these two drugs violates the Linpinski’s rule of 5, and is very different from current aanti-tubercular drugs. So these two drugs would be quite unlikely to be selected as lead compounds for the inhibition of InhA, using common drug discovery methods.
The authors also compared the difference of the binding poses of the compounds to COMT and InhA, and pointed out possible ways to optimize them so that they can have weaker affinity to the original target COMT.
To summarize, this paper successfully presents a case study of using chemical systems biology, in particular using protein-ligand interactions, to assist drug discovery in a new multi-target-multi-drug paradigm.
