Abstract :

The Rapid Expansion Of Biological Sequence Repositories Has Created A Significant Demand For Computational Approaches Capable Of Identifying The Functional Relationships Among Newly Discovered Proteins. Protein Family Classification Is An Important Task In Bioinformatics Because Proteins Grouped Within The Same Family Often Exhibit Related Structural Characteristics, Evolutionary Patterns, And Biological Functions. Conventional Classification Procedures Commonly Depend On Sequence Alignment, Similarity Searching, Profile Models, And Manually Formulated Descriptors. Although These Techniques Have Contributed Substantially To Protein Annotation, Their Performance May Decline When Dealing With Highly Divergent Sequences, Previously Unseen Patterns, And Continuously Expanding Biological Databases. Deep Learning Provides An Alternative Computational Paradigm By Automatically Deriving Discriminative Representations Directly From Amino Acid Sequences. This Paper Presents A Detailed Survey And Analytical Framework For Protein Family Classification Using Deep Learning Techniques. Major Architectures, Including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) Networks, Gated Recurrent Units (GRUs), Attention-based Models, And Transformer Architectures, Are Examined With Respect To Their Ability To Model Protein Sequences. The Study Discusses Sequence Encoding Strategies, Embedding Representations, Local Motif Extraction, Long-range Dependency Learning, And Classification Mechanisms. The Applicability Of Large Protein Databases Such As Pfam And UniProt For Model Development Is Also Examined. Furthermore, Traditional Bioinformatics Approaches Are Compared With Deep Neural Models In Terms Of Feature Engineering Requirements, Computational Scalability, Sequence Dependency Modelling, And Classification Capability.

Published:

13-1-2026

Issue:

Vol. 26 No. 1 (2026)

Page Nos:

269-284

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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