AIMC Topic: DNA-Binding Proteins

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Deep learning-based high-resolution time inference for deciphering dynamic gene regulation from fixed embryos.

Nature communications
Embryo development is driven by the spatiotemporal dynamics of complex gene regulatory networks. Uncovering these dynamics requires simultaneous tracking of multiple fluctuating molecular species over time, which exceeds the capabilities of tradition...

TRIM29 alleviates intervertebral disc degeneration through the PI3K/AKT/mTOR pathway.

Scientific reports
Intervertebral disc degeneration (IDD), a prevalent spinal condition linked to low back pain, has substantial genetic components, necessitating deeper understanding of its mechanisms. This study categorized nucleus pulposus cell (NPC) populations and...

Divergence in a eukaryotic transcription factor's co-TF dependence involves multiple intrinsically disordered regions.

Nature communications
Combinatorial control by transcription factors (TFs) is central to eukaryotic gene regulation, yet its mechanism, evolution, and regulatory impact are not well understood. Here we use natural variation in the yeast phosphate starvation (PHO) response...

TET2 gene mutation status associated with poor prognosis of transition zone prostate cancer: a retrospective cohort study based on whole exome sequencing and machine learning models.

Frontiers in endocrinology
BACKGROUND: Prostate cancer (PCa) in the transition zone (TZ) is uncommon and often poses challenges for early diagnosis, but its genomic determinants and therapeutic vulnerabilities remain poorly characterized.

A comprehensive review of computational methods for Protein-DNA binding site prediction.

Analytical biochemistry
Accurately identifying protein-DNA binding sites is essential for understanding the molecular mechanisms underlying biological processes, which in turn facilitates advancements in drug discovery and design. While biochemical experiments provide the m...

TransBind allows precise detection of DNA-binding proteins and residues using language models and deep learning.

Communications biology
Identifying DNA-binding proteins and their binding residues is critical for understanding diverse biological processes, but conventional experimental approaches are slow and costly. Existing machine learning methods, while faster, often lack accuracy...

Artificial intelligence-powered prediction of AIM-2 inflammasome sequences using transformers and graph attention networks in periodontal inflammation.

Scientific reports
Periodontal inflammation is a chronic condition affecting the tissues surrounding teeth. Initiated by dental plaque, it triggers an immune response leading to tissue destruction. The AIM-2 inflammasome regulates this response, and understanding its p...

Further Development of SAMPDI-3D: A Machine Learning Method for Predicting Binding Free Energy Changes Caused by Mutations in Either Protein or DNA.

Genes
BACKGROUND/OBJECTIVES: Predicting the effects of protein and DNA mutations on the binding free energy of protein-DNA complexes is crucial for understanding how DNA variants impact wild-type cellular function. As many cellular interactions involve pro...

Computational staining of CD3/CD20 positive lymphocytes in human tissues with experimental confirmation in a genetically engineered mouse model.

Frontiers in immunology
INTRODUCTION: Immune dysregulation plays a major role in cancer progression. The quantification of lymphocytic spatial inflammation may enable spatial system biology, improve understanding of therapeutic resistance, and contribute to prognostic imagi...