Fourches Lab Journal Club: Favorite April Research Articles

Xinhao's favorite article for the month of April was “Luciferase Advisor: High-Accuracy Model To Flag False Positive Hits in Luciferase HTS Assays”.

 

The recognition of false positives in firefly luciferase assays has been a challenge for a long time. In this JCINF article, Ghosh et al. built various chemoinformatic models to identify the false positive molecules.

 

Phyo Phyo's favorite article for the month of April was "Protocols for Requirement-Driven Protein Design in the Rosetta Modeling Program."

 

In this JCIM article, Guffy et al. incorporated into the molecular modeling program Rosetta a method called "SEWING" which generates diverse sets of protein backbones by combining pieces of naturally occurring proteins.

 

 

Bethany's favorite article was "Identification of Selective, Cell Active Inhibitors of Protein Arginine Methyltransferase 5 through Structure-Based Virtual Screening and Biological Assays."

 

From computers to cells, advancements are being made in the fight against lymphoma and leukemia one step at a time. In this JCINF article Ye et al. increased the odds of inhabitation of PRMT5 with molecular docking and experimentally induced cell cycle arrest.

 

George was really impressed by the research creativity and determination of the authors in this Science Advances publication, "Accelerated discovery of metallic glasses through iteration of machine learning and high-throughput experiments."

 

What I really liked about this study was that the authors were not deterred from using Machine Learning to identify metallic glasses by a small data set. Instead, they chose to build their own data set, developed from high-throughput experiments, to train and refine their model.

 

Jeremy's favorite article for the month of April was "Models and estimators linking individual-based and sample-based rarefaction, extrapolation and comparison of assemblages"

 

Ecologists routinely use a "hit enrichment" like curve to identify minimum number of samples necessary to estimate species abundance in an area. These statistical methods may be useful in cheminformatics for maximizing the chemical diversity in hits identified by virtual screening.

 

 

 

 

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