Protein Conformation Generation via Force-Guided SE(3) Diffusion Models (2403.14088v2)

Abstract: The conformational landscape of proteins is crucial to understanding their functionality in complex biological processes. Traditional physics-based computational methods, such as molecular dynamics (MD) simulations, suffer from rare event sampling and long equilibration time problems, hindering their applications in general protein systems. Recently, deep generative modeling techniques, especially diffusion models, have been employed to generate novel protein conformations. However, existing score-based diffusion methods cannot properly incorporate important physical prior knowledge to guide the generation process, causing large deviations in the sampled protein conformations from the equilibrium distribution. In this paper, to overcome these limitations, we propose a force-guided SE(3) diffusion model, ConfDiff, for protein conformation generation. By incorporating a force-guided network with a mixture of data-based score models, ConfDiff can generate protein conformations with rich diversity while preserving high fidelity. Experiments on a variety of protein conformation prediction tasks, including 12 fast-folding proteins and the Bovine Pancreatic Trypsin Inhibitor (BPTI), demonstrate that our method surpasses the state-of-the-art method.

• The paper introduces ConfDiff, a force-guided SE(3) diffusion model that generates realistic protein conformations without relying on MD training data.
• It employs an intermediate force-guidance strategy, integrating MD force fields to favor low-energy, physically plausible structures.
• Experimental results show ConfDiff achieves higher conformation diversity and quality compared to state-of-the-art methods.

Enhanced Protein Conformation Generation with Force-Guided SE(3) Diffusion Models

Introduction

Protein dynamics play a crucial role in most biological processes, with protein conformational changes being a pivotal aspect. Traditional methods for protein conformation sampling, such as Molecular Dynamics (MD) simulations, despite being detailed, face limitations in sampling efficiency and capturing rare events. Emerging deep generative models, particularly diffusion models, present a promising alternative for generating novel protein conformations. These models, however, often miss incorporating crucial physical priors, which results in deviations from realistic protein dynamics. Addressing this, we propose a force-guided SE(3) diffusion model, termed ConfDiff, aimed at generating protein conformations with high fidelity and diversity, aligned with the equilibrium Boltzmann distribution.

Methodology

Baseline Model Construction

We establish a baseline diffusion model combining a sequence-conditional model with an unconditional model using classifier-free guidance on SE(3). This strategy is devised to balance the conformation quality with diversity. Unlike existing models that rely heavily on MD data for training, ConfDiff does not necessitate such data, broadening its applicability.

Incorporation of Force-Guided Sampling

A novel addition to our method is the employment of a force-guided approach during the diffusion sampling phase. This is achieved through the construction of a force-guided network alongside a mixture of score models. By applying MD force fields as a physics-based preference function, we emphasize generating conformations with lower potential energy. This preference significantly boosts the chances of sampling more accurate protein conformations that resonate with physical realities. Notably, ConfDiff introduces an intermediate force guidance strategy into the reverse-time diffusion process, making it the inaugural force-guided network catering to protein conformation generation.

SE(3) Diffusion Process

The SE(3) diffusion process, designed for protein backbone generation, treats translations and rotations independently, promoting a more nuanced sampling process. It adapts contrasting noise schedules for translation and rotation, accommodating the distinctiveness of protein conformations.

Experimental Insights

The efficacy of ConfDiff is evaluated across various benchmarks, where it exhibits consistent superiority over contemporary state-of-the-art models. Specifically, our method demonstrates the ability to generate more diverse sample sets without compromising their quality, as indicated by improved scores across standard evaluation metrics. This success underscores the advantage of integrating physical priors via force-guided diffusion processes in enhancing the generation of biologically plausible protein conformations.

Theoretical Underpinning

Critical to our approach is the theoretical grounding provided by adapting a contrastive energy prediction (CEP) framework, which allows the integration of physical priors seamlessly. Our leverage of the MD energy function to inform the diffusion process exemplifies the practical application of this theory, affording our model an edge in generating energetically favorable protein conformations.

Future Directions

While ConfDiff lays a promising foundation for protein conformation generation through diffusion models, future research could explore enhancing its efficiency, especially concerning the computational demands of full-atom energy evaluations. Furthermore, refining the force-guided diffusion process to facilitate even more accurate sampling of conformational states remains an enticing prospect.

Conclusion

Conclusively, ConfDiff represents a significant stride forward in the generation of protein conformations employing diffusion models. By melding sequence-conditional modeling with force-guided diffusion, informed by physical priors, this method opens new corridors in accurately predicting protein dynamics, potentially benefiting various biological and pharmaceutical research endeavors.