Structure Language Models for Protein Conformation Generation (2410.18403v2)

Abstract: Proteins adopt multiple structural conformations to perform their diverse biological functions, and understanding these conformations is crucial for advancing drug discovery. Traditional physics-based simulation methods often struggle with sampling equilibrium conformations and are computationally expensive. Recently, deep generative models have shown promise in generating protein conformations as a more efficient alternative. However, these methods predominantly rely on the diffusion process within a 3D geometric space, which typically centers around the vicinity of metastable states and is often inefficient in terms of runtime. In this paper, we introduce Structure LLMing (SLM) as a novel framework for efficient protein conformation generation. Specifically, the protein structures are first encoded into a compact latent space using a discrete variational auto-encoder, followed by conditional LLMing that effectively captures sequence-specific conformation distributions. This enables a more efficient and interpretable exploration of diverse ensemble modes compared to existing methods. Based on this general framework, we instantiate SLM with various popular LM architectures as well as proposing the ESMDiff, a novel BERT-like structure LLM fine-tuned from ESM3 with masked diffusion. We verify our approach in various scenarios, including the equilibrium dynamics of BPTI, conformational change pairs, and intrinsically disordered proteins. SLM provides a highly efficient solution, offering a 20-100x speedup than existing methods in generating diverse conformations, shedding light on promising avenues for future research.

• The paper proposes a new SLM framework leveraging discrete VAEs and conditional language modeling to efficiently generate diverse protein conformations with a 20–100× speedup over traditional methods.
• It introduces ESMDiff, a BERT-like structure language model fine-tuned from ESM3 using a masked diffusion framework, demonstrating the adaptability of large language models for biological applications.
• The research offers significant implications for drug discovery and structural biology by enabling real-time exploration of protein conformational ensembles.

Structure LLMs for Protein Conformation Generation

The paper presents a framework for protein conformation generation using Structure LLMing (SLM). This approach addresses the limitations of traditional physics-based simulations, which are often computationally expensive and inefficient in sampling equilibrium conformations. The authors propose leveraging deep generative models, specifically discrete variational autoencoders, to encode protein structures into a latent space, followed by LLMing to capture sequence-specific conformation distributions.

Methodology

The SLM framework encompasses an initial encoding of protein structures into a compact, discrete latent space, utilizing a discrete variational auto-encoder. This process forms the foundation for generating "structure languages," capturing diverse conformational states effectively. The core of the methodology lies in conditional LLMing that operates over these latent representations. By conditioning on input sequences, this approach allows for efficient exploration of alternative conformational ensembles.

The introduction of ESMDiff, a BERT-like structure LLM, marks a notable advancement. This model, fine-tuned from ESM3 using a masked diffusion framework, showcases the adaptability of existing LLMs for biological applications.

Performance and Numerical Results

Experimental evaluation across multiple scenarios, including BPTI equilibrium dynamics and intrinsically disordered proteins, demonstrates the effectiveness of the proposed SLM framework. The authors report a substantial speedup of 20-100x over existing methods in generating conformational diversity. This efficiency is primarily attributable to the model's capacity to learn and generate a broad range of structures without exhaustive geometric computations.

Implications and Future Directions

The implications of applying LLMs to protein conformations are profound. By circumventing the challenges associated with modeling in high-dimensional geometric spaces, this framework opens new avenues for real-time applications in drug discovery and structural biology.

Despite showcasing promising results, the paper highlights potential directions for improvement, such as tailoring continuous latent spaces or optimizing latent space configurations for even more efficient generative modeling.

Conclusion

This research lays a solid foundation for future inquiry and application of LLMs in protein structure generation. The integration of existing large-scale LLMs with novel latent encoding strategies offers a robust and scalable solution, moving a step closer to practical, efficient generation of protein conformations. As the field evolves, further exploration into the interplay between generative models and complex biological structures will undoubtedly yield additional breakthroughs.