Prot2Token: A Unified Framework for Protein Modeling via Next-Token Prediction (2505.20589v1)

Abstract: The diverse nature of protein prediction tasks has traditionally necessitated specialized models, hindering the development of broadly applicable and computationally efficient Protein LLMs (PLMs). In this work, we introduce Prot2Token, a unified framework that overcomes these challenges by converting a wide spectrum of protein-related predictions, from sequence-level properties and residue-specific attributes to complex inter-protein interactions, into a standardized next-token prediction format. At its core, Prot2Token employs an autoregressive decoder, conditioned on embeddings from pre-trained protein encoders and guided by learnable task tokens, to perform diverse predictions. This architecture uniquely facilitates multi-task learning, enabling a single model to master numerous tasks with improved efficiency. We present extensive experimental validation across a variety of benchmarks, demonstrating Prot2Tokens strong predictive power in different types of protein-prediction tasks. Key results include significant speedups (e.g., near 1000x over AlphaFold2 with MSA) and performance often matching or exceeding specialized approaches. Beyond that, we introduce an auxiliary self-supervised decoder pre-training approach to improve spatially sensitive task performance. Prot2Token thus offers a significant step towards a versatile, high-throughput paradigm for protein modeling, promising to accelerate biological discovery and the development of novel therapeutics. The code is available at https://github.com/mahdip72/prot2token .

Prot2Token: A Unified Framework for Protein Modeling

Overview

Prot2Token introduces a unified framework for addressing diverse protein prediction tasks through next-token prediction, effectively streamlining the protein modeling process. The paper addresses the challenges posed by traditional models requiring specialization and inefficiency in handling various protein language tasks, such as sequence-level properties, residue-specific attributes, and protein-protein interactions. Prot2Token aims to bridge these tasks into a unified format using an autoregressive decoder, conditioned by pre-trained protein encoders supplemented with learnable task tokens, fostering multi-task learning capability within a single model.

Methodology

The Prot2Token framework leverages encoder-decoder transformers, where protein sequences are encoded using a bidirectional transformer, ESM2, and optionally incorporate a chemical encoder (BARTSmile) for protein-ligand interactions. Embedding alignment with a causal decoder facilitates multi-task predictions through a standardized tokenization approach. The model’s architectural design empowers it to convert diverse labels into consistent token sequences, thus facilitating broad-spectrum prediction tasks under consistent protocols.

Experimental Results

Extensive benchmarking demonstrates Prot2Token’s proficiency across classification, regression, binding site, sequence-to-sequence, and other complex protein prediction tasks. Highlights include substantial improvements in speed (e.g., ~1000x faster than AlphaFold2 with MSA input), and strong performance matching or surpassing specialized approaches in tasks such as secondary structure prediction, mutation stability assessment, and localization prediction.

The paper further iterates on a self-supervised pre-training strategy for the decoder, which enhances spatially sensitive task performance addressing initial limitations in tasks reliant on precise spatial understanding, such as binding site prediction.

Implications

Prot2Token offers significant implications for both theoretical and practical protein modeling. The framework espouses higher efficiency and accessibility in computational biology, potentially accelerating biological discoveries and therapeutic developments. By integrating multi-task learning advancements, it promises a transformative leap toward versatile protein modeling workflows.

Beyond its utility in protein prediction, Prot2Token paves the way for the integration of protein generation capabilities by refining LLMs to manage complex prediction tasks, opening avenues for broader applications within synthetic biology and drug design.

Future Directions

Future prospects involve exploring comprehensive multi-task training that harnesses the synergistic potential across diverse protein tasks, improving decoding strategies, and advancing protein design methodologies. Enhanced protein modeling through Prot2Token could revolutionize in silico processes, culminating in cohesive, high-throughput systems for targeted therapeutic development.