Results 181 to 190 of about 370,636 (381)
A peptide library screening platform has been developed to discover covalent transcription factor inhibitors. It identifies an antagonist cysteine residue, which forms an inhibitory disulphide with cJun C269. Conversion of this cysteine to an electrophile generates an irreversible and selective cJun inhibitor, validating this approach for covalent ...
Andrew Brennan+3 more
wiley +1 more source
Reactions Sequence of Leucine Activation Catalysed by Leucyl-RNA Synthetase. 1. Kinetic Studies [PDF]
Pierre Rouget, F. Chapeville
openalex +1 more source
LGR6 overexpression ameliorates cardiac hypertrophy by regulating metabolic reprogramming through USP4‐PPARα pathway. Abstract Metabolic reprogramming is a pivotal mechanism in the pathogenesis of pathological cardiac hypertrophy. Leucine‐rich repeat‐containing G protein‐coupled receptor 6 (Lgr6) has emerged as a significant player in cardiovascular ...
Mengmeng Zhao+7 more
wiley +1 more source
Machine learning discoveries of ASCL2-X synergy in ETC-1922159 treated colorectal cancer cells [PDF]
Achaete-scute complex homolog 2 (ASCL2) codes a part of the basic helix-loop-helix (BHLH) transcription factor family. WNTs have been found to directly affect the stemness of the tumor cells via regulation of ASCL2. Switching off the ASCL2 literally blocks the stemness process of the tumor cells and vice versa.
arxiv
DiffMC‐Gen: A Dual Denoising Diffusion Model for Multi‐Conditional Molecular Generation
DiffMC‐Gen, a dual‐diffusion model for 2D and 3D molecular generation, simultaneously optimizes multiple key objectives across the drug design process, enabling the generation of novel, target‐specific small‐molecule ligands with high therapeutic potential.
Yuwei Yang+7 more
wiley +1 more source
A Putative Leucine-Rich Repeat Receptor Kinase Involved in Brassinosteroid Signal Transduction
Jianming Li, J. Chory
semanticscholar +1 more source
Explainable Deep Multilevel Attention Learning for Predicting Protein Carbonylation Sites
Selective carbonylation sites (SCANS) are conceptualized, designed, evaluated, and released. SCANS captures segment‐level, protein‐level, and residue embeddings features. It utilizes elaborate loss function to penalize cross‐predictions at the residue level.
Jian Zhang+6 more
wiley +1 more source