Research Toolbox

The Schwartz lab aims to provide tools that use experimental data combined with computational approaches to better understand CK2 activity. The ion channel predictor identifies possible CK2 phosphorylation sites of both wild type and K198R CK2. The quantitative proteomics database shares the results from our TMT-labeled mass spectrometry experiments looking at five human cell lines, four of which contain a variant CK2.

Signor is a resource that annotates experimental evidence about causal interactions between proteins and other entities of biological relevance: stimuli, phenotypes, enzyme inhibitors, complexes, protein families etc. Each entry points to the experimental evidence supporting the interaction and is enriched by additional relevant metadata such as the effect of the interaction on the activity of the target entity, the molecular mechanism underlying this effect, etc.

1520 FDA-approved & EMA-approved drugs for HTS and HCS screening 

A team of in-house medicinal chemists and pharmacists has committed to selecting highly relevant screening compounds for >25 years, to offer a screening collection of off-patent drugs with high chemical and pharmacological diversity, as well as known bioavailability and safety in humans. 

To date, CMap has generated a library containing over 1.5M gene expression profiles from ~5,000 small-molecule compounds, and ~3,000 genetic reagents, tested in multiple cell types. To produce data of that scale, we’ve developed L1000, a relatively inexpensive and rapid high-throughput gene expression profiling technology. Expression data are processed through a computational pipeline that converts raw fluorescence intensity into signatures, which can be used to query the CMap database for perturbations that give a related gene expression response. 

AlphaFold is an AI system developed by DeepMind that predicts a protein’s 3D structure from its amino acid sequence. It regularly achieves accuracy competitive with experiment.

AlphaMissense is an adaptation of AlphaFold fine-tuned on human and primate variant population frequency databases to predict missense variant pathogenicity. By combining structural context and evolutionary conservation, the model achieves state-of-the-art results across a wide range of genetic and experimental benchmarks, all without explicitly training on such data.