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Related Concept Videos

Conserved Binding Sites01:49

Conserved Binding Sites

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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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Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
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Ligand Binding and Linkage00:49

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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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Related Experiment Video

Updated: Aug 16, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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Deep learning for MYC binding site recognition.

R Fioresi1, P Demurtas1, G Perini1

  • 1Department of Pharmacy and Biotechnology, Alma Mater Studiorum, Bologna, Italy.

Frontiers in Bioinformatics
|December 22, 2022
PubMed
Summary
This summary is machine-generated.

Predicting Myc transcription factor binding sites using DeeperBind computational tools can accelerate cancer research. This approach aids in understanding Myc

Keywords:
MYCbinding siteconsensus sequencedeep learning—artificial neural networkmachine learning and AI

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Area of Science:

  • Genomics and Bioinformatics
  • Cancer Biology
  • Computational Biology

Background:

  • Myc is a crucial oncogene implicated in over 80% of cancers, regulating numerous genes.
  • Determining Myc's genome distribution is vital for predicting its activity and guiding cancer therapy.
  • Experimental methods like chromatin immunoprecipitation are time-consuming and not suitable for individual patient tumor analysis.

Purpose of the Study:

  • To develop and validate an in silico tool for rapid and accurate prediction of Myc transcription factor binding sites.
  • To explore the potential of machine learning for personalized cancer diagnostics and treatment strategies.

Main Methods:

  • Utilized the DeeperBind computational tool integrated with DeepRAM on the Google Colab platform.
  • Trained DeeperBind models using data from multiple cell lines to predict Myc binding sites.
  • Analyzed the filters of trained DeeperBind models to identify associated DNA consensus sequences.

Main Results:

  • DeeperBind accurately predicted Myc binding sites with an Area Under the Curve (AUC) exceeding 0.96.
  • Identified the canonical CACGTG sequence for Myc, along with G/C box and TGGGA sequences bound by SP1 and MIZ-1.
  • The findings suggest DeeperBind can identify binding sites for factors involved in Myc's repressive functions.

Conclusions:

  • Machine learning tools like DeeperBind offer a powerful synergy with experimental biology, significantly reducing experimental time.
  • This approach can guide experimental design and accelerate the development of personalized cancer therapies.
  • In silico prediction of transcription factor binding sites holds promise for advancing precision oncology.