GPCR-SLM: Small Language Model-Based Classification of GPCRs Using Knowledge Distillation Technique.
TL;DR
A scalable machine learning framework GPCR SLM is presented, that classifies GPCRs across 86 distinct families using a lightweight transformer model optimized through knowledge distillation, and demonstrates the effectiveness of combining distilled protein language models with flexible classification frameworks for high-resolution functional annotation.
Abstract
Accurate protein family classification is essential since proteins within the same family share conserved structural domains and biochemical functions that deter mine their biological roles. G protein-coupled receptors (GPCRs) represent one of the largest and most diverse protein families in eukaryotes, serving as targets for ap proximately 35% of FDA-approved drugs. While traditional sequence alignment methods, such as BLAST, provide foundational tools for identifying homologous sequences, they exhibit limited accuracy in distinguishing closely related GPCR families with low sequence homology. Recently, deep learning approaches offer promising accuracy; however, they employ fixed-size classification architectures that force newly discovered protein families into pre-existing categories, preventing the recognition of novel families and limiting scalability as the protein universe expands. In this work, we present a scalable machine learning framework GPCR SLM, that classifies GPCRs across 86 distinct families using a lightweight transformer model optimized through knowledge distillation. Our approach achieved an overall ac curacy of 99%, significantly outperforming BLAST (86.1%) and HMMER (91%), while demonstrating substantial computational efficiency with an average speedup of 33.5× compared to large protein language models. These results demonstrate the effectiveness of combining distilled protein language models with flexible classification frameworks for high-resolution functional annotation.