Genomic language model predicts protein co-regulation and function.

Deciphering the relationship between a gene and its genomic context is fundamental to understanding and engineering biological systems. Machine learning has shown promise in learning latent relationships underlying the sequence-structure-function paradigm from massive protein sequence datasets. However, to date, limited attempts have been made in extending this continuum to include higher order genomic context information. Evolutionary processes dictate the specificity of genomic contexts in which a gene is found across phylogenetic distances, and these emergent genomic patterns can be leveraged to uncover functional relationships between gene products. Here, we train a genomic language model (gLM) on millions of metagenomic scaffolds to learn the latent functional and regulatory relationships between genes. gLM learns contextualized protein embeddings that capture the genomic context as well as the protein sequence itself, and encode biologically meaningful and functionally relevant information (e.g. enzymatic function, taxonomy). Our analysis of the attention patterns demonstrates that gLM is learning co-regulated functional modules (i.e. operons). Our findings illustrate that gLM's unsupervised deep learning of the metagenomic corpus is an effective and promising approach to encode functional semantics and regulatory syntax of genes in their genomic contexts and uncover complex relationships between genes in a genomic region.

Decoding the Parkinson's Symphony: PARIS, Maestro of Transcriptional Regulation and Metal Coordination for Dopamine Release.

Parkin interacting substrate (PARIS) is a pivotal transcriptional regulator in the brain that orchestrates the activity of various enzymes through its intricate interactions with biomolecules, including nucleic acids. Notably, the binding of PARIS to insulin response sequences (IRSs) triggers a cascade of events that results in the functional loss in the substantia nigra, which impairs dopamine release and, subsequently, exacerbates the relentless neurodegeneration. Here, we report the details of the interactions of PARIS with IRSs via classical zinc finger (ZF) domains in PARIS, namely, PARIS(ZF2-4). Our biophysical studies with purified PARIS(ZF2-4) elucidated the binding partner of PARIS, which generates specific interactions with the IRS1 (5'-TATTTTT, Kd = 38.9 ± 2.4 nM) that is positioned in the promoter region of peroxisome proliferator-activated receptor gamma coactivator-1α (PGC-1α). Mutational and metal-substitution studies demonstrated that Zn(II)-PARIS(ZF2-4) could recognize its binding partner selectively. Overall, our work provides submolecular details regarding PARIS and shows that it is a transcriptional factor that regulates dopamine release. Thus, PARIS could be a crucial target for therapeutic applications.

DW71177: A novel [1,2,4]triazolo[4,3-a]quinoxaline-based potent and BD1-Selective BET inhibitor for the treatment of acute myeloid leukemia.

The bromodomain and extraterminal domain (BET) family proteins recognize acetyl-lysine (Kac) at the histone tail through two tandem bromodomains, i.e., BD1 and BD2, to regulate gene expression. BET proteins are attractive therapeutic targets in cancer due to their involvement in oncogenic transcriptional activation, and bromodomains have defined Kac-binding pockets. Here, we present DW-71177, a potent BET inhibitor that selectively interacts with BD1 and exhibits strong antileukemic activity. X-ray crystallography, isothermal titration calorimetry, and molecular dynamic studies have revealed the robust and specific binding of DW-71177 to the Kac-binding pocket of BD1. DW-71177 effectively inhibits oncogenes comparable to the pan-BET inhibitor OTX-015, but with a milder impact on housekeeping genes. It efficiently blocks cancer-associated transcriptional changes by targeting genes that are highly enriched with BRD4 and histone acetylation marks, suggesting that BD1-selective targeting could be an effective and safe therapeutic strategy against leukemia.