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Conserved Binding Sites01:49

Conserved Binding Sites

5.2K
Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...
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Cis-regulatory Sequences02:02

Cis-regulatory Sequences

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Cis-regulatory sequences are short fragments of non-coding DNA that are present on the same chromosomes as the genes that they regulate. These fragments serve as binding sites for transcriptional regulators, proteins that are responsible for controlling gene transcription and differential gene expression across cell types in eukaryotes. Cis-regulatory sequences can be close to the gene of interest or thousands of bases away in the DNA sequence; however, those sequences that are further away are...
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Cis-regulatory Sequences02:02

Cis-regulatory Sequences

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Covalently Linked Protein Regulators02:04

Covalently Linked Protein Regulators

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Proteins can undergo many types of post-translational modifications, often in response to changes in their environment. These modifications play an important role in the function and stability of these proteins. Covalently linked molecules include functional groups, such as methyl, acetyl, and phosphate groups, and also small proteins, such as ubiquitin. There are around 200 different types of covalent regulators that have been identified.
These groups modify specific amino acids in a protein....
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Ligand Binding and Linkage00:49

Ligand Binding and Linkage

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Allosteric proteins have more than one ligand binding site; the binding of a ligand to any of these sites influences the binding of ligands to the other sites. When a protein is allosteric, its binding sites are called coupled or linked.  In the case of enzymes, the site that binds to the substrate is known as the active site and the other site is known as the regulatory site. When a ligand binds to the regulatory site, this leads to conformational changes in the protein that can influence...
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Cooperative Binding of Transcription Regulators02:13

Cooperative Binding of Transcription Regulators

7.4K
Transcriptional regulators bind to specific cis-regulatory sequences in the DNA to regulate gene transcription. These cis-regulatory sequences are very short, usually less than ten nucleotide pairs in length. The short length means that there is a high probability of the exact same sequence randomly occurring throughout the genome.  Since regulators can also bind to groups of similar sequences, this further increases the chances of random binding. Transcriptional regulators form...
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Related Experiment Video

Updated: Feb 19, 2026

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA
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Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA

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Noncoding Variants Functional Prioritization Methods Based on Predicted Regulatory Factor Binding Sites.

Haoyue Fu1, , Xiangde Zhang1

  • 1College of Sciences, Northeastern University, Shenyang, China.

Current Genomics
|October 31, 2017
PubMed
Summary
This summary is machine-generated.

Deep learning models enhance the prediction of transcription factor binding sites (TFBSs), aiding in understanding gene mutation impacts on diseases. This research highlights deep convolution neural networks for identifying functional noncoding variants.

Keywords:
Deep convolution neural networkNoncoding variantsSaturated mutagenesisSingle nucleotide polymorphismsTranscription factor binding sites

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Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes

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Area of Science:

  • Genomics and Bioinformatics
  • Computational Biology
  • Molecular Genetics

Background:

  • Post-genomic research increasingly relies on studying gene networks and gene-protein interactions to understand disease mechanisms.
  • Transcription factor binding sites (TFBSs) are crucial for gene expression and regulation, making their identification a key research area.
  • Deep learning methods show superior performance in predicting the function of noncoding variants compared to conventional approaches.

Purpose of the Study:

  • To review conventional methods for TFBSs identification.
  • To discuss deep learning approaches, specifically deep convolution neural networks (CNNs), for TFBSs prediction.
  • To explore techniques for identifying functional noncoding variants from de novo sequences.

Main Methods:

  • Review and analysis of conventional TFBSs identification techniques.
  • Exploration of deep learning models, including deep CNNs, for TFBSs prediction.
  • Summary of methods for identifying functional noncoding variants from sequence data.

Main Results:

  • Conventional TFBSs identification methods were reviewed from various angles.
  • Deep learning methods, particularly deep CNNs, show promise for TFBSs prediction, with discussions on data preprocessing, network architecture, and performance metrics.
  • Techniques for identifying functional noncoding variants from de novo sequences were summarized.

Conclusions:

  • High-throughput assays and increased data/chromatin features will improve deep CNN accuracy for TFBSs identification.
  • Further understanding of deep CNN frameworks can enhance model performance and lead to more suitable designs.
  • Prioritization models using deep CNN predictions can help elucidate the role of gene mutations in diseases.