Publications by authors named "T C Lazar"

Article Synopsis
  • * R-DPRs bind much stronger to the protein G3BP1 than RNA does, promoting the formation of cellular droplets through a process called liquid-liquid phase separation (LLPS), and these droplets can eventually aggregate harmful proteins linked to ALS.
  • * Differences in pathology between two types of R-DPRs, poly-GR and poly-PR, suggest that poly-GR primarily targets G3BP1 in stress granules, rather than NPM1 in nucleoli, indicating
View Article and Find Full Text PDF

Protein cis-regulatory elements (CREs) are regions that modulate the activity of a protein through intramolecular interactions. Kinases, pivotal enzymes in numerous biological processes, often undergo regulatory control via inhibitory interactions in cis. This study delves into the mechanisms of cis regulation in kinases mediated by CREs, employing a combined structural and sequence analysis.

View Article and Find Full Text PDF

The essential G-cyclin, CCND1, is frequently overexpressed in cancer, contributing to tumorigenesis by driving cell-cycle progression. D-type cyclins are rate-limiting regulators of G-S progression in mammalian cells via their ability to bind and activate CDK4 and CDK6. In addition, cyclin D1 conveys kinase-independent transcriptional functions of cyclin D1.

View Article and Find Full Text PDF

Short Linear Motifs (SLiMs) are the smallest structural and functional components of modular eukaryotic proteins. They are also the most abundant, especially when considering post-translational modifications. As well as being found throughout the cell as part of regulatory processes, SLiMs are extensively mimicked by intracellular pathogens.

View Article and Find Full Text PDF

The Protein Ensemble Database (PED) (URL: https://proteinensemble.org) is the primary resource for depositing structural ensembles of intrinsically disordered proteins. This updated version of PED reflects advancements in the field, denoting a continual expansion with a total of 461 entries and 538 ensembles, including those generated without explicit experimental data through novel machine learning (ML) techniques.

View Article and Find Full Text PDF