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Conservation of Protein Domains Over Different Proteins

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Protein domains are small structurally independent units that are part of a single amino acid chain. Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...

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Updated: May 26, 2025

A Comparative Approach to Characterize the Landscape of Host-Pathogen Protein-Protein Interactions

A Comparative Approach to Characterize the Landscape of Host-Pathogen Protein-Protein Interactions

Published on: July 18, 2013

An optimized deep-forest algorithm using a modified differential evolution optimization algorithm: A case of

Jerry Emmanuel^1,2,3, Itunuoluwa Isewon^1,2,3, Jelili Oyelade^1,2,3

¹Department of Computer and Information Sciences, Covenant University, Ota, Nigeria.

Computational and Structural Biotechnology Journal

|February 25, 2025

Summary

A novel modified Differential Evolution (DE) method enhances Deep Forest models for host-pathogen protein-protein interaction prediction, improving accuracy and efficiency.

Keywords:

Deep forest Differential evolution Hyperparameter Optimization Plasmodium falciparum Protein-protein interaction

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Last Updated: May 26, 2025

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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

Area of Science:

Bioinformatics
Computational Biology
Machine Learning

Background:

Deep Forest models offer adaptive feature learning but suffer from manual hyperparameter tuning and inefficiencies.
Bayesian optimization is a standard hyperparameter tuning method, often enhanced by evolutionary algorithms like Differential Evolution (DE).
Traditional DE methods randomly select vectors, leading to suboptimal solutions in optimization.

Purpose of the Study:

To develop a modified Differential Evolution (DE) acquisition function for improved hyperparameter optimization in Deep Forest models.
To enhance the prediction of host-pathogen protein-protein interactions using an optimized Deep Forest model.
To address the limitations of random vector selection in DE for more effective optimization.

Main Methods:

A modified DE acquisition function with a weighted and adaptive donor vector technique was developed.
This optimized DE approach was integrated into a Deep Forest model for automatic hyperparameter tuning.
The model was evaluated on human-Plasmodium falciparum protein sequence data using 10-fold cross-validation.

Main Results:

The optimized Deep Forest model achieved 89.3% accuracy, with 85.4% sensitivity and 91.6% precision.
The model outperformed standard optimization methods and other machine learning models across all evaluated metrics.
Seven novel host-pathogen interactions were predicted, and the model was deployed as a web application.

Conclusions:

The modified DE acquisition function significantly improves Deep Forest model performance for host-pathogen PPI prediction.
The developed approach offers an efficient and accurate method for hyperparameter optimization in complex biological predictions.
The accessible web application facilitates further research and application of the optimized model.