PEPE: scalable extraction of multi-modal protein language model representations

Area of Science:

• Computational Biology
• Bioinformatics
• Machine Learning in Biology

Background:

• Protein language models (PLMs) capture complex amino-acid relationships, yielding embeddings rich in structural, functional, and evolutionary data.
• Existing workflows for extracting these embeddings often involve arbitrary choices, leading to suboptimal representations and hindering downstream analyses.
• Large-scale embedding generation faces computational and memory bottlenecks due to inefficient accumulation and redundant computations.

Purpose of the Study:

• To introduce PEPE (Parallel Extraction for Protein Embeddings), a tool designed for efficient, high-throughput, and multimodal extraction of protein embeddings.
• To overcome the limitations of current methods in terms of speed, memory consumption, and scalability for generating protein representations.
• To enable researchers to efficiently generate large, informative embedding datasets for discovering optimal representations for various biological tasks.

Main Methods:

• PEPE utilizes a parallelized and streaming-based architecture to significantly accelerate embedding extraction.
• The tool maintains stable, low memory consumption, overcoming the linear scaling issues of conventional methods.
• PEPE offers a flexible interface supporting a wide range of state-of-the-art and custom PLMs.

Main Results:

• PEPE achieves runtimes several orders of magnitude faster than sequential embedding extraction approaches.
• The tool enables multimodal embedding extraction with minimal memory footprint, even exceeding available RAM.
• PEPE streamlines the generation of diverse embedding configurations, facilitating the identification of high-performing latent states.

Conclusions:

• PEPE provides a scalable, robust, and user-friendly solution for efficient protein embedding generation.
• The tool empowers researchers to create massive, information-rich embedding datasets for downstream structural, functional, and evolutionary analyses.
• By optimizing embedding generation, PEPE facilitates the discovery of context-specific latent states without additional computational cost.