Predicting drug targets; modelling heart mitochondria; assessing drug efficacy and safety; problems with predicting enhancer-promoter pairs.
Check out our Editors-in-Chief’s selection of papers from the December issue of PLOS Computational Biology.
Predicting protein targets for drug-like compounds using transcriptomics
Bioactive compounds often disrupt cellular gene expression in ways that are difficult to predict. While the correlation between a cellular response after treatment with a small molecule and the knockdown of its target protein should be simple to establish, in practice this goal has been difficult to achieve. The main challenges are that data are noisy, drugs are not intended to be active in all cell types, and signals from a bona fide target(s) may be obscured by correlations with knockdowns of other proteins in the same pathway(s). Here, Nicolas Pabon and Colleagues found that a random forest classification model can detect meaningful correlational patterns when gene expression profiles after compound treatment and gene knockdowns in four or more cell lines are compared. When this approach is enriched by a structure-based screen, novel drug-target interactions can be predicted.
Insights on the impact of mitochondrial organisation on bioenergetics in high-resolution computational models of cardiac cell architecture
Mammalian cardiomyocytes contain a high volume of mitochondria, which maintains the continuous and bulk supply of ATP to sustain normal heart function. Previously, cardiac mitochondria were understood to be distributed in a regular, crystalline pattern, which facilitated a steady supply of ATP at different workloads. Using electron microscopy images of cell cross sections these authors recently found that they are not regularly distributed inside cardiomyocytes. Here, Shouryadipta Ghosh and colleagues created a new spatially accurate computational models of cardiac cell bioenergetics and tested whether this heterogeneous distribution of mitochondria causes non-uniform energy supply and contractile force production in the cardiomyocyte.
PathFX provides mechanistic insights into drug efficacy and safety for regulatory review and therapeutic development
Many drugs fail to reach the market because they are not sufficiently efficacious for their disease indication or they cause intolerable side-effects. To understand drug efficacy and safety, Jennifer Wilson and colleagues created an algorithm, PathFX. The algorithm identified relationships between drugs and diseases, and drugs and side-effects. They tested PathFX’s ability to identify the disease for which the drug was developed and then applied PathFX to post-marketing reports of drug side effects and identified drug side effects where regulatory review was ambiguous. Finally, they identified novel diseases for which marketed drugs could treat. The method has the potential to be a tool for assessing drug safety and efficacy during development and may have utility for regulators and industry scientists.
Local epigenomic state cannot discriminate interacting and non-interacting enhancer–promoter pairs with high accuracy
Wang Xi and Michael Beer report an experimental design issue in recent machine learning formulations of the enhancer-promoter interaction problem arising from the fact that many enhancer-promoter pairs share features. Cross-fold validation schemes which do not correctly separate these feature sharing enhancer-promoter pairs into one test set report high accuracy, which is actually arising from high training set accuracy and a failure to properly evaluate generalization performance. Cross-fold validation schemes which properly segregate pairs with shared features show markedly reduced ability to predict enhancer-promoter interactions from epigenomic state. Parameter scans with multiple models indicate that local epigenomic features of individual pairs of enhancers and promoters cannot distinguish those pairs that interact from those which do with high accuracy, suggesting that additional information is required to predict enhancer-promoter interactions.
One Thousand Simple Rules
Finally, check out our recent ‘One Thousand Simple Rules’ article written by Philip E. Bourne, Fran Lewitter, Scott Markel, Jason A. Papin: “What began as a one-off in 2005 as Ten Simple Rules for Getting Published  has, in thirteen years, now multiplied a hundredfold to become One Thousand Simple Rules for many aspects of one’s professional development and led to Quick Tips in the journal’s Education section. This milestone of a thousand rules has been reached thanks to the unselfish work of all stakeholders—authors, editors, reviewers, and readers.