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Protein-protein Interfaces

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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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Genome-wide Protein-protein Interaction Screening by Protein-fragment Complementation Assay PCA in Living Cells

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Published on: March 3, 2015

Exploring the Power of Machine Learning in Analysing Protein-Protein Sequences.

Anindya Nag¹, Riya Sil², Md Mehedi Hassan³

¹Department of Computer Science & Engineering, Northern University of Business and Technology Khulna, Khulna, Bangladesh.

IET Systems Biology

|April 22, 2026

Summary

This summary is machine-generated.

This review summarizes computational methods for protein sequence analysis, focusing on AI-driven techniques like machine learning and deep learning for predicting protein structure and interactions. It aids researchers in selecting suitable tools for advancing molecular biology, disease research, and drug discovery.

Keywords:

biomedical engineering data mining feature extraction feature selection principal component analysis proteins statistical analysis

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Area of Science:

Molecular Biology
Bioinformatics
Computational Biology

Background:

Proteins are essential macromolecules whose functions are dictated by amino acid sequences.
Understanding the sequence-structure-function relationship is a key challenge in molecular biology.
Experimental methods are accurate but costly and time-consuming, driving the need for computational approaches.

Purpose of the Study:

To provide a comprehensive review of recent computational techniques for protein sequence analysis.
To focus on machine learning (ML) and deep learning (DL) frameworks for predicting protein structure and interactions.
To aid researchers in selecting appropriate computational tools.

Main Methods:

Systematic review and categorization of computational techniques.
Focus on ML and DL-based frameworks.
Comparative analysis of methodologies, datasets, and performance.

Main Results:

Categorization of existing approaches based on methodological foundations, datasets, and performance.
Discussion of advantages and limitations of various computational techniques.
Identification of research gaps and emerging trends in AI-driven protein modeling.

Conclusions:

The review offers a structured reference for selecting computational techniques in protein sequence analysis.
Facilitates advancements in protein structure prediction and interaction analysis.
Supports progress in disease research and drug discovery through AI-driven protein modeling.