EnerBridge-DPO: Energy-Guided Protein Inverse Folding with Markov Bridges and Direct Preference Optimization

• URL: http://arxiv.org/abs/2506.09496v1
• Date: Wed, 11 Jun 2025 08:12:26 GMT
• Title: EnerBridge-DPO: Energy-Guided Protein Inverse Folding with Markov Bridges and Direct Preference Optimization
• Authors: Dingyi Rong, Haotian Lu, Wenzhuo Zheng, Fan Zhang, Shuangjia Zheng, Ning Liu,
• Abstract summary: This work aims to overcome the limitation by developing a model that directly generates low-energy, stable protein sequences.<n>We propose EnerBridge-DPO, a novel inverse folding framework focused on generating low-energy, high-stability protein sequences.<n>Our evaluations demonstrate that EnerBridge-DPO can design protein complex sequences with lower energy while maintaining sequence recovery rates comparable to state-of-the-art models.
• Score: 8.642286608437344
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Designing protein sequences with optimal energetic stability is a key challenge in protein inverse folding, as current deep learning methods are primarily trained by maximizing sequence recovery rates, often neglecting the energy of the generated sequences. This work aims to overcome this limitation by developing a model that directly generates low-energy, stable protein sequences. We propose EnerBridge-DPO, a novel inverse folding framework focused on generating low-energy, high-stability protein sequences. Our core innovation lies in: First, integrating Markov Bridges with Direct Preference Optimization (DPO), where energy-based preferences are used to fine-tune the Markov Bridge model. The Markov Bridge initiates optimization from an information-rich prior sequence, providing DPO with a pool of structurally plausible sequence candidates. Second, an explicit energy constraint loss is introduced, which enhances the energy-driven nature of DPO based on prior sequences, enabling the model to effectively learn energy representations from a wealth of prior knowledge and directly predict sequence energy values, thereby capturing quantitative features of the energy landscape. Our evaluations demonstrate that EnerBridge-DPO can design protein complex sequences with lower energy while maintaining sequence recovery rates comparable to state-of-the-art models, and accurately predicts $\Delta \Delta G$ values between various sequences.

Related papers

• Protein Inverse Folding From Structure Feedback [78.27854221882572]
  We introduce a novel approach to fine-tune an inverse folding model using feedback from a protein folding model.<n>Our results on the CATH 4.2 test set demonstrate that DPO fine-tuning leads to a significant improvement in average TM-Score.
  arXiv  Detail & Related papers  (2025-06-03T16:02:12Z)
• Bridge-IF: Learning Inverse Protein Folding with Markov Bridges [32.53142367116483]
  Inverse protein folding is a fundamental task in computational protein design. We propose Bridge-IF, a generative diffusion bridge model for inverse folding. We show that Bridge-IF predominantly surpasses existing baselines in sequence recovery and excels in the design of plausible proteins with high foldability.
  arXiv  Detail & Related papers  (2024-11-04T14:35:14Z)
• Antigen-Specific Antibody Design via Direct Energy-based Preference Optimization [51.28231365213679]
  We tackle antigen-specific antibody sequence-structure co-design as an optimization problem towards specific preferences. We propose direct energy-based preference optimization to guide the generation of antibodies with both rational structures and considerable binding affinities to given antigens.
  arXiv  Detail & Related papers  (2024-03-25T09:41:49Z)
• Joint Age-based Client Selection and Resource Allocation for Communication-Efficient Federated Learning over NOMA Networks [8.030674576024952]
  In federated learning (FL), distributed clients can collaboratively train a shared global model while retaining their own training data locally. In this paper, a joint optimization problem of client selection and resource allocation is formulated, aiming to minimize the total time consumption of each round in FL over a non-orthogonal multiple access (NOMA) enabled wireless network. In addition, a server-side artificial neural network (ANN) is proposed to predict the FL models of clients who are not selected at each round to further improve FL performance.
  arXiv  Detail & Related papers  (2023-04-18T13:58:16Z)
• An Energy-Based Prior for Generative Saliency [62.79775297611203]
  We propose a novel generative saliency prediction framework that adopts an informative energy-based model as a prior distribution. With the generative saliency model, we can obtain a pixel-wise uncertainty map from an image, indicating model confidence in the saliency prediction. Experimental results show that our generative saliency model with an energy-based prior can achieve not only accurate saliency predictions but also reliable uncertainty maps consistent with human perception.
  arXiv  Detail & Related papers  (2022-04-19T10:51:00Z)
• A Probabilistic Forecast-Driven Strategy for a Risk-Aware Participation in the Capacity Firming Market [30.828362290032935]
  This paper addresses the energy management of a grid-connected renewable generation plant and a battery energy storage device in the capacity firming market. A recently developed deep learning model known as normalizing flows is used to generate quantile forecasts of renewable generation.
  arXiv  Detail & Related papers  (2021-05-28T13:13:07Z)
• EBM-Fold: Fully-Differentiable Protein Folding Powered by Energy-based Models [53.17320541056843]
  We propose a fully-differentiable approach for protein structure optimization, guided by a data-driven generative network. Our EBM-Fold approach can efficiently produce high-quality decoys, compared against traditional Rosetta-based structure optimization routines.
  arXiv  Detail & Related papers  (2021-05-11T03:40:29Z)
• AEGD: Adaptive Gradient Descent with Energy [0.0]
  We propose AEGD, a new algorithm for first-order gradient non-energy objective functions variable. We show energy-dependent AEGD for both non-energy convergence and desired small step size.
  arXiv  Detail & Related papers  (2020-10-10T22:17:27Z)
• Adaptive pruning-based optimization of parameterized quantum circuits [62.997667081978825]
  Variisy hybrid quantum-classical algorithms are powerful tools to maximize the use of Noisy Intermediate Scale Quantum devices. We propose a strategy for such ansatze used in variational quantum algorithms, which we call "Efficient Circuit Training" (PECT) Instead of optimizing all of the ansatz parameters at once, PECT launches a sequence of variational algorithms.
  arXiv  Detail & Related papers  (2020-10-01T18:14:11Z)
• Energy-based models for atomic-resolution protein conformations [88.68597850243138]
  We propose an energy-based model (EBM) of protein conformations that operates at atomic scale. The model is trained solely on crystallized protein data. An investigation of the model's outputs and hidden representations finds that it captures physicochemical properties relevant to protein energy.
  arXiv  Detail & Related papers  (2020-04-27T20:45:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.