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Related Experiment Video

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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

SEMal: Accurate protein malonylation site predictor using structural and evolutionary information.

Shubhashis Roy Dipta¹, Ghazaleh Taherzadeh², Md Wakil Ahmad¹

¹Department of Computer Science and Engineering, United International University, Dhaka, Bangladesh.

Computers in Biology and Medicine

|October 6, 2020

Summary

Researchers developed SEMal, a computational tool to identify Malonylation sites on proteins. This method uses structural and evolutionary features, offering a faster and more accurate alternative to experimental detection for understanding biological pathways.

Keywords:

Evolutionary features Malonylation Post-translational modifications Predicted local structure Rotation forest Structural features

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Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

Area of Science:

Biochemistry
Bioinformatics
Proteomics

Background:

Post-translational modifications (PTMs) regulate protein function and biological interactions.
Malonylation is a recently identified PTM impacting glucose and fatty acid metabolism.
Experimental detection of Malonylation via mass spectrometry is costly and time-consuming.

Purpose of the Study:

To develop an efficient and accurate computational method for identifying Malonylation sites in protein sequences.
To introduce a novel approach, SEMal, for Malonylation site prediction.
To leverage structural and evolutionary features for improved prediction accuracy.

Main Methods:

Utilized a combination of structural and evolutionary features from protein sequences.
Employed the Rotation Forest (RoF) classification algorithm for prediction.
Developed SEMal, a novel computational predictor for Malonylation sites.

Main Results:

SEMal achieved high performance metrics, including sensitivity and accuracy.
For Homo Sapiens, SEMal demonstrated a sensitivity of 0.94 and accuracy of 0.94.
For Mus Musculus, SEMal showed a sensitivity of 0.89 and accuracy of 0.91.
SEMal outperformed existing methods in key performance indicators.

Conclusions:

SEMal provides a computationally efficient and highly accurate method for identifying Malonylation sites.
The approach offers a valuable alternative to experimental methods for studying Malonylation.
SEMal is available as a publicly accessible online predictor, facilitating further research.