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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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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: Nov 24, 2025

Specificity Analysis of Protein Lysine Methyltransferases Using SPOT Peptide Arrays
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Mal-Light: Enhancing Lysine Malonylation Sites Prediction Problem Using Evolutionary-based Features.

Wakil Ahmad1, Easin Arafat1, Ghazaleh Taherzadeh2

  • 1Department of Computer Science and Engineering, United International University, United City, Madani Avenue, Dhaka 1212, Bangladesh.

IEEE Access : Practical Innovations, Open Solutions
|December 23, 2020
PubMed
Summary
This summary is machine-generated.

A new computational method, Mal-Light, accurately predicts malonylation sites on proteins. This post-translational modification is crucial for biological interactions and energy metabolism, and Mal-Light offers a fast, cost-effective alternative to experimental identification.

Keywords:
Cluster Centroid based Majority Under-sampling TechniqueEvolutionary InformationLight Gradient BoostingLysine MalonylationMachine LearningPost Transla tional Modifications

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Area of Science:

  • Biochemistry
  • Computational Biology
  • Genomics

Background:

  • Post-translational modifications (PTMs) significantly alter protein function in all domains of life.
  • Malonylation, a recently identified PTM, plays a critical role in biological interactions and energy metabolism.
  • Experimental identification of PTM sites is resource-intensive, necessitating efficient computational approaches.

Purpose of the Study:

  • To develop a novel machine learning-based computational method for predicting malonylation sites.
  • To improve the accuracy and efficiency of identifying malonylation modifications in proteins.
  • To provide a publicly accessible tool for malonylation site prediction.

Main Methods:

  • A bi-peptide based approach was used to extract local evolutionary information from amino acid sequences.
  • Light Gradient Boosting (LightGBM) was employed as the machine learning classifier.
  • The model, named Mal-Light, was trained and validated on protein data from Homo Sapiens and Mus Musculus.

Main Results:

  • Mal-Light achieved high performance metrics, including Matthew's correlation coefficient (MCC) of 0.74 (H. Sapiens) and 0.60 (M. Musculus).
  • The model demonstrated significant improvements in prediction accuracy (86.66% and 79.51%), sensitivity (78.26% and 67.27%), and specificity (95.05% and 91.75%) compared to existing methods.
  • The developed predictor is available online for public use.

Conclusions:

  • Mal-Light represents a significant advancement in the computational prediction of malonylation sites.
  • The method offers a fast and cost-effective solution for identifying this important post-translational modification.
  • This tool can accelerate research into the biological roles of malonylation in various species.