Applications of proteochemometrics - from species extrapolation to cell line sensitivity modelling

HPLC coupled with quadrupole time of flight tandem mass spectrometry for analysis of glycosylated components from the fresh flowers of two congeneric species: Robinia hispida L. and Robinia pseudoacacia L

Developing methods for the systematic and rapid identification of the chemical compositions of fresh plant tissues has long attracted the attention of phytochemists and pharmacologists. In the present study, based on highly efficient sample pretreatment and high-throughput analysis of high-performance liquid chromatography coupled with quadrupole time of flight tandem mass spectrometry data using molecular networks, a method was developed for systematically analyzing the chemical constituents of the fresh flowers of Robinia hispida L. and Robina pseudoacacia L., two congeneric ornamental species that lack prior consideration. A total of 44 glycosylated structures were characterized. And on the basis of establishing of the fragmentation pathways of 11 known flavonoid glycosides, together with the molecular networking analysis, 18 other ions of flavonoid glycosides in five classes were clustered. Moreover, 15 soyasaponins/triterpenoid glycosides were tentatively identified by comparison of their tandem mass spectrometry characteristic ions with those reported in the literature or the online Global Natural Product Social Molecular Networking database. The water extracts were separated by flash chromatography, which resulted in the discovery of one new compound, named rohispidascopolin, along with five known entities. The pharmacological targets were predicted by SwissTargetPrediction.

Evaluation of deep and shallow learning methods in chemogenomics for the prediction of drugs specificity

Chemogenomics, also called proteochemometrics, covers a range of computational methods that can be used to predict protein–ligand interactions at large scales in the protein and chemical spaces. They differ from more classical ligand-based methods (also called QSAR) that predict ligands for a given protein receptor. In the context of drug discovery process, chemogenomics allows to tackle the question of predicting off-target proteins for drug candidates, one of the main causes of undesirable side-effects and failure within drugs development processes. The present study compares shallow and deep machine-learning approaches for chemogenomics, and explores data augmentation techniques for deep learning algorithms in chemogenomics. Shallow machine-learning algorithms rely on expert-based chemical and protein descriptors, while recent developments in deep learning algorithms enable to learn abstract numerical representations of molecular graphs and protein sequences, in order to optimise the performance of the prediction task. We first propose a formulation of chemogenomics with deep learning, called the chemogenomic neural network (CN), as a feed-forward neural network taking as input the combination of molecule and protein representations learnt by molecular graph and protein sequence encoders. We show that, on large datasets, the deep learning CN model outperforms state-of-the-art shallow methods, and competes with deep methods with expert-based descriptors. However, on small datasets, shallow methods present better prediction performance than deep learning methods. Then, we evaluate data augmentation techniques, namely multi-view and transfer learning, to improve the prediction performance of the chemogenomic neural network. We conclude that a promising research direction is to integrate heterogeneous sources of data such as auxiliary tasks for which large datasets are available, or independently, multiple molecule and protein attribute views.

Gene isoforms as expression-based biomarkers predictive of drug response in vitro

Next-generation sequencing technologies have recently been used in pharmacogenomic studies to characterize large panels of cancer cell lines at the genomic and transcriptomic levels. Among these technologies, RNA-sequencing enable profiling of alternatively spliced transcripts. Given the high frequency of mRNA splicing in cancers, linking this feature to drug response will open new avenues of research in biomarker discovery. To identify robust transcriptomic biomarkers for drug response across studies, we develop a meta-analytical framework combining the pharmacological data from two large-scale drug screening datasets. We use an independent pan-cancer pharmacogenomic dataset to test the robustness of our candidate biomarkers across multiple cancer types. We further analyze two independent breast cancer datasets and find that specific isoforms of IGF2BP2, NECTIN4, ITGB6, and KLHDC9 are significantly associated with AZD6244, lapatinib, erlotinib, and paclitaxel, respectively. Our results support isoform expressions as a rich resource for biomarkers predictive of drug response.

Highlights from the Third International Society for Computational Biology (ISCB) European Student Council Symposium 2014

In this meeting report, we give an overview of the talks, presentations and posters presented at the third European Symposium of the International Society for Computational Biology (ISCB) Student Council. The event was organized as a satellite meeting of the 13th European Conference for Computational Biology (ECCB) and took place in Strasbourg, France on September 6th, 2014.