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Related Concept Videos

Conserved Binding Sites01:49

Conserved Binding Sites

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 analyses the...

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m6A-TCPred: a web server to predict tissue-conserved human m6A sites using machine learning approach.

Gang Tu1, Xuan Wang2,3, Rong Xia4

  • 1Department of Biological Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, 215123, China.

BMC Bioinformatics
|March 26, 2024
PubMed
Summary

Researchers developed m6A-TCPred, a tool to identify tissue-conserved N6-methyladenosine (m6A) sites. This computational method distinguishes conserved from non-conserved m6A residues across 23 human tissues.

Keywords:
Gene ontologyMachine learningSupport vector machineWeb serverm6A modification

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Area of Science:

  • Molecular Biology
  • Epigenetics
  • Bioinformatics

Background:

  • N6-methyladenosine (m6A) is a prevalent RNA modification critical for gene regulation.
  • Dysregulation of m6A is implicated in various diseases, including cancer.
  • Existing prediction tools lack the ability to identify tissue-conserved m6A sites at base resolution.

Purpose of the Study:

  • To develop a computational tool for predicting tissue-conserved m6A residues.
  • To differentiate between tissue-conserved and non-conserved m6A modifications.
  • To provide a resource for studying the conservation of m6A modifications across human tissues.

Main Methods:

  • Utilized m6A profiling data from 23 human tissues.
  • Employed sequence-based characteristics and genome-derived information.
  • Developed a computational tool named m6A-TCPred.

Main Results:

  • m6A-TCPred successfully identified distinct patterns of tissue-conserved m6A modifications.
  • Achieved an average AUROC of 0.871 (cross-validation) and 0.879 (independent datasets).
  • The tool effectively distinguishes conserved from non-conserved m6A sites.

Conclusions:

  • Integrated findings into an online platform with a database of 268,115 high-confidence m6A sites.
  • Developed a web server for predicting the conserved status of user-provided m6A data.
  • The m6A-TCPred web interface is publicly accessible for research use.