Mining Chemical Activity Status from High-Throughput Screening Assays

High-throughput screening (HTS) experiments provide a valuable resource that reports biological activity of numerous chemical compounds relative to their molecular targets. Building computational models that accurately predict such activity status (active vs. inactive) in specific assays is a challenging task given the large volume of data and frequently small proportion of active compounds relative to the inactive ones. We developed a method, DRAMOTE, to predict activity status of chemical compounds in HTP activity assays. For a class of HTP assays, our method achieves considerably better results than the current state-of-the-art-solutions. We achieved this by modification of a minority oversampling technique. To demonstrate that DRAMOTE is performing better than the other methods, we performed a comprehensive comparison analysis with several other methods and evaluated them on data from 11 PubChem assays through 1,350 experiments that involved approximately 500,000 interactions between chemicals and their target proteins. As an example of potential use, we applied DRAMOTE to develop robust models for predicting FDA approved drugs that have high probability to interact with the thyroid stimulating hormone receptor (TSHR) in humans. Our findings are further partially and indirectly supported by 3D docking results and literature information. The results based on approximately 500,000 interactions suggest that DRAMOTE has performed the best and that it can be used for developing robust virtual screening models. The datasets and implementation of all solutions are available as a MATLAB toolbox online at www.cbrc.kaust.edu.sa/dramote and can be found on Figshare.     

Disproportionate Contributions of Select Genomic Compartments and Cell Types to Genetic Risk for Coronary Artery Disease

Large genome-wide association studies (GWAS) have identified many genetic loci associated with risk for myocardial infarction (MI) and coronary artery disease (CAD). Concurrently, efforts such as the National Institutes of Health (NIH) Roadmap Epigenomics Project and the Encyclopedia of DNA Elements (ENCODE) Consortium have provided unprecedented data on functional elements of the human genome. In the present study, we systematically investigate the biological link between genetic variants associated with this complex disease and their impacts on gene function. First, we examined the heritability of MI/CAD according to genomic compartments. We observed that single nucleotide polymorphisms (SNPs) residing within nearby regulatory regions show significant polygenicity and contribute between 59–71% of the heritability for MI/CAD. Second, we showed that the polygenicity and heritability explained by these SNPs are enriched in histone modification marks in specific cell types. Third, we found that a statistically higher number of 45 MI/CAD-associated SNPs that have been identified from large-scale GWAS studies reside within certain functional elements of the genome, particularly in active enhancer and promoter regions. Finally, we observed significant heterogeneity of this signal across cell types, with strong signals observed within adipose nuclei, as well as brain and spleen cell types. These results suggest that the genetic etiology of MI/CAD is largely explained by tissue-specific regulatory perturbation within the human genome.     

DWFS: A Wrapper Feature Selection Tool Based on a Parallel Genetic Algorithm

Many scientific problems can be formulated as classification tasks. Data that harbor relevant information are usually described by a large number of features. Frequently, many of these features are irrelevant for the class prediction. The efficient implementation of classification models requires identification of suitable combinations of features. The smaller number of features reduces the problem’s dimensionality and may result in higher classification performance. We developed DWFS, a web-based tool that allows for efficient selection of features for a variety of problems. DWFS follows the wrapper paradigm and applies a search strategy based on Genetic Algorithms (GAs). A parallel GA implementation examines and evaluates simultaneously large number of candidate collections of features. DWFS also integrates various filtering methods that may be applied as a pre-processing step in the feature selection process. Furthermore, weights and parameters in the fitness function of GA can be adjusted according to the application requirements. Experiments using heterogeneous datasets from different biomedical applications demonstrate that DWFS is fast and leads to a significant reduction of the number of features without sacrificing performance as compared to several widely used existing methods. DWFS can be accessed online at www.cbrc.kaust.edu.sa/dwfs.     

Flow-cytometric analysis of mouse embryonic stem cell lipofection using small and large DNA constructs

Using the lipofection reagent LipofectAMINE 2000 we have examined the delivery of plasmid DNA (5-200 kb) to mouse embryonic stem (mES) cells by flow cytometry. To follow the physical uptake of lipoplexes we labeled DNA molecules with the fluorescent dye TOTO-1. In parallel, expression of an EGFP reporter cassette in constructs of different sizes was used as a measure of nuclear delivery. The cellular uptake of DNA lipoplexes is dependent on the uptake competence of mES cells, but it is largely independent of DNA size. In contrast, nuclear delivery was reduced with increasing plasmid size. In addition, linear DNA is transfected with lower efficiency than circular DNA. Inefficient cytoplasmic trafficking appears to be the main limitation in the nonviral delivery of large DNA constructs to the nucleus of mES cells. Overcoming this limitation should greatly facilitate functional studies with large genomic fragments in embryonic stem cells.     

Identifying Cognitive States Using Regularity Partitions

Functional Magnetic Resonance (fMRI) data can be used to depict functional connectivity of the brain. Standard techniques have been developed to construct brain networks from this data; typically nodes are considered as voxels or sets of voxels with weighted edges between them representing measures of correlation. Identifying cognitive states based on fMRI data is connected with recording voxel activity over a certain time interval. Using this information, network and machine learning techniques can be applied to discriminate the cognitive states of the subjects by exploring different features of data. In this work we wish to describe and understand the organization of brain connectivity networks under cognitive tasks. In particular, we use a regularity partitioning algorithm that finds clusters of vertices such that they all behave with each other almost like random bipartite graphs. Based on the random approximation of the graph, we calculate a lower bound on the number of triangles as well as the expectation of the distribution of the edges in each subject and state. We investigate the results by comparing them to the state of the art algorithms for exploring connectivity and we argue that during epochs that the subject is exposed to stimulus, the inspected part of the brain is organized in an efficient way that enables enhanced functionality.     

Loading mechanisms of ring helicases at replication origins

Threading of DNA through the central channel of a replicative ring helicase is known as helicase loading, and is a pivotal event during replication initiation at replication origins. Once loaded, the helicase recruits the primase through a direct protein-protein interaction to complete the initial 'priming step' of DNA replication. Subsequent assembly of the polymerases and processivity factors completes the structure of the replisome. Two replisomes are assembled, one on each strand, and move in opposite directions to replicate the parental DNA during the 'elongation step' of DNA replication. Replicative helicases are the motor engines of replisomes powered by the conversion of chemical energy to mechanical energy through ATP binding and hydrolysis. Bidirectional loading of two ring helicases at a replication origin is achieved by strictly regulated and intricately choreographed mechanisms, often through the action of replication initiation and helicase-loader proteins. Current structural and biochemical data reveal a wide range of different helicase-loading mechanisms. Here we review advances in this area and discuss their implications.     

E-cadherin's dark side: Possible role in tumor progression

In the context of cancer, E-cadherin has traditionally been categorized as a tumor suppressor, given its essential role in the formation of proper intercellular junctions, and its downregulation in the process of epithelial–mesenchymal transition (EMT) in epithelial tumor progression. Germline or somatic mutations in the E-cadherin gene (CDH1) or downregulation by epigenetic mechanisms have been described in a small subset of epithelial cancers. However, recent evidence also points toward a promoting role of E-cadherin in several aspects of tumor progression. This includes preserved (or increased) E-cadherin expression in microemboli of inflammatory breast carcinoma, a possible “mesenchymal to epithelial transition” (MET) in ovarian carcinoma, collective cell invasion in some epithelial cancers, a recent association of E-cadherin expression with a more aggressive brain tumor subset, as well as the intriguing possibility of E-cadherin involvement in specific signaling networks in the cytoplasm and/or nucleus. In this review we address a lesser-known, positive role for E-cadherin in cancer.