Belharra Therapeutics is the next wave in chemoproteomics focused on applying a novel chemistry enabled non-covalent probe library and quantitative mass spectrometry to identify chemical probes that selectively bind any pocket, on any protein, in live cells. The platform is identifying chemical probes that selectively engage diverse protein classes including transcription factors, adaptors, ion channels, and transporters. Most proteins identified as being selectively engaged by our probe library do not have a reported ligand in drug bank demonstrating the ability of the platform to identify novel pockets and potential chemical probe starting points for these targets.

This presentation explores new reactive moieties for protein labeling through a series of vignettes. It focuses on the mechanistic analysis and proteome-wide labeling capabilities of cyclobutyl diazirine probes and the large-scale proteomic analysis of diazirine photoaffinity probes—as well as the broad utility of tetrazines as bioorthogonal handles, in addition to protein labeling applications.   

Condensates regulate cellular processes by concentrating and organizing biomolecules in time and space. Condensate aberrations serve as central nodes of dysfunction in many diseases. Viewing condensates as drug targets opens untapped opportunities to influence high value, "undruggable" drug targets. Our AI-powered discovery platform supports identification of condensate-drivers of diseases, development of disease-faithful models, and discovery & development of c-mods—fueling programs across multiple therapeutic areas, including oncology and neurodegeneration.

The urgent need for effective therapeutics against coronaviruses has prompted the development of innovative drug discovery approaches. In this study, we established a robust high-content screening platform to identify small molecules capable of modulating the condensation of the SARS-CoV-2 Nucleocapsid (N) protein. Our promising results demonstrate that perturbing viral condensate dynamics can generate effective antiviral drugs for multiple viral species, including SARS-CoV-2.

QUANTRO developed QUANTROseq, an innovative platform able to map small molecules to their targets by matching transcriptional fingerprints produced by drug candidates with the ones obtained by controlled acute degradation of the target-of-interest. Using this technology at-scale, QUANTRO demonstrated that time-resolved transcriptional profiling reveals drugs´ mode-of-action with unprecedent precision and sensitivity and enables discovery of NCE against previously undruggable targets.

Clickable covalent compounds are some of the most valuable small-molecules one could pursue, given their numerous applications. However, merging a ligand/protein-recognition element, a protein-reactive functional group, and a bioorthogonal chemistry reporter group into a single chemical probe is non-trivial. This talk will highlight some of our work toward a universal toolbox of valuable building blocks researchers can use to synthesize clickable covalent probes for chemical biology-mediated drug discovery and development.

Genes encoding chromatin proteins are among the top mutated cancer genes. Interpreting the functional significance for every cancer mutation remains a major obstacle for advancing cancer research. Using Polycomb repressive complex 2 (PRC2) as a case study, I will showcase the utility of pooled tiling base-editing screens to define regulatory regions and understand the functional significance of cancer-associated mutations in their endogenous context.

The complexity of the ubiquitin-proteasome system makes it challenging to systematically pair proteasomal substrates to their cognate E3 ubiquitin ligases. We have developed new technologies that are able to identify ubiquitin-proteasome targets on a genome-wide scale and can pair these substrates to E3s using multiplex CRISPR screening. Additionally, saturating mutagenesis and computational approaches allow us to define the molecular basis for many degradative pathways at scale.

Assessment of the functional effects of DNA sequence variation is crucial for an understanding of the genetic basis of human disease. We applied the newest versions of base editors, coupled with the PAM-flexible Cas9, for high-throughput interrogation of genetic variation at endogenous loci. High-coverage tiling enabled mapping of the functional regions in the proteins and discovering potential gain-of-function mutations across a panel of genes with strong cancer dependencies.

We have developed a computational, genetics-based approach which integrates functional data from GWAS, ontologies, and biological networks to predict potential drug targets with evidence for disease association. We obtained a list of target genes associated with ALS and prioritized potential target genes by integrating structural information and cell-type transcriptomics data.

Interaction-based screening and design are promising novel strategies for hit finding and lead optimization. The key information unit in the interaction realm is a thin interface between the interacting ligand and protein. When fed to deep neural networks, interaction signatures may help to infer structure-activity relationships for unliganded proteins by exploiting structural data across ligands and proteins. In this talk, we will give an overview of the approach and describe its successful applications to several challenging targets.

Identification of hot spots on the surface of macromolecules is key to evaluating the druggability of novel targets and the likelihood of finding new chemical entities. Computational hot-spot mapping was performed across a set of drug targets, and a machine learning approach was employed to select the top druggable sites. Within this context, the utility of AI-generated protein models obtained using AlphaFold, including ensembles of structural models, was assessed.