Pan-protein Design Learning Enables Task-adaptive Generalization for Low-resource Enzyme Design

• URL: http://arxiv.org/abs/2411.17795v1
• Date: Tue, 26 Nov 2024 17:51:33 GMT
• Title: Pan-protein Design Learning Enables Task-adaptive Generalization for Low-resource Enzyme Design
• Authors: Jiangbin Zheng, Ge Wang, Han Zhang, Stan Z. Li,
• Abstract summary: We present CrossDesign, a domain-adaptive framework that leverages pretrained protein language models (PPLMs) By aligning protein structures with sequences, CrossDesign transfers pretrained knowledge to structure models, overcoming the limitations of limited structural data. Experimental results highlight CrossDesign's superior performance and robustness, especially with out-of-domain enzymes.
• Score: 44.258193520999484
• License:
• Abstract: Computational protein design (CPD) offers transformative potential for bioengineering, but current deep CPD models, focused on universal domains, struggle with function-specific designs. This work introduces a novel CPD paradigm tailored for functional design tasks, particularly for enzymes-a key protein class often lacking specific application efficiency. To address structural data scarcity, we present CrossDesign, a domain-adaptive framework that leverages pretrained protein language models (PPLMs). By aligning protein structures with sequences, CrossDesign transfers pretrained knowledge to structure models, overcoming the limitations of limited structural data. The framework combines autoregressive (AR) and non-autoregressive (NAR) states in its encoder-decoder architecture, applying it to enzyme datasets and pan-proteins. Experimental results highlight CrossDesign's superior performance and robustness, especially with out-of-domain enzymes. Additionally, the model excels in fitness prediction when tested on large-scale mutation data, showcasing its stability.

Related papers

• MeToken: Uniform Micro-environment Token Boosts Post-Translational Modification Prediction [65.33218256339151]
  Post-translational modifications (PTMs) profoundly expand the complexity and functionality of the proteome. Existing computational approaches predominantly focus on protein sequences to predict PTM sites, driven by the recognition of sequence-dependent motifs. We introduce the MeToken model, which tokenizes the micro-environment of each acid, integrating both sequence and structural information into unified discrete tokens.
  arXiv  Detail & Related papers  (2024-11-04T07:14:28Z)
• Endowing Protein Language Models with Structural Knowledge [5.587293092389789]
  We introduce a novel framework that enhances protein language models by integrating protein structural data. The refined model, termed Protein Structure Transformer (PST), is further pretrained on a small protein structure database. PST consistently outperforms the state-of-the-art foundation model for protein sequences, ESM-2, setting a new benchmark in protein function prediction.
  arXiv  Detail & Related papers  (2024-01-26T12:47:54Z)
• A Hierarchical Training Paradigm for Antibody Structure-sequence Co-design [54.30457372514873]
  We propose a hierarchical training paradigm (HTP) for the antibody sequence-structure co-design. HTP consists of four levels of training stages, each corresponding to a specific protein modality. Empirical experiments show that HTP sets the new state-of-the-art performance in the co-design problem.
  arXiv  Detail & Related papers  (2023-10-30T02:39:15Z)
• Functional Geometry Guided Protein Sequence and Backbone Structure Co-Design [12.585697288315846]
  We propose a model to jointly design Protein sequence and structure based on automatically detected functional sites. NAEPro is powered by an interleaving network of attention and equivariant layers, which can capture global correlation in a whole sequence. Experimental results show that our model consistently achieves the highest amino acid recovery rate, TM-score, and the lowest RMSD among all competitors.
  arXiv  Detail & Related papers  (2023-10-06T16:08:41Z)
• Generative Pretrained Autoregressive Transformer Graph Neural Network applied to the Analysis and Discovery of Novel Proteins [0.0]
  We report a flexible language-model based deep learning strategy, applied here to solve complex forward and inverse problems in protein modeling. The model is applied to predict secondary structure content (per-residue level and overall content), protein solubility, and sequencing tasks. We find that adding additional tasks yields emergent synergies that the model exploits in improving overall performance.
  arXiv  Detail & Related papers  (2023-05-07T12:30:24Z)
• Structure-informed Language Models Are Protein Designers [69.70134899296912]
  We present LM-Design, a generic approach to reprogramming sequence-based protein language models (pLMs) We conduct a structural surgery on pLMs, where a lightweight structural adapter is implanted into pLMs and endows it with structural awareness. Experiments show that our approach outperforms the state-of-the-art methods by a large margin.
  arXiv  Detail & Related papers  (2023-02-03T10:49:52Z)
• Protein Sequence and Structure Co-Design with Equivariant Translation [19.816174223173494]
  Existing approaches generate both protein sequence and structure using either autoregressive models or diffusion models. We propose a new approach capable of protein sequence and structure co-design, which iteratively translates both protein sequence and structure into the desired state. Our model consists of a trigonometry-aware encoder that reasons geometrical constraints and interactions from context features. All protein amino acids are updated in one shot in each translation step, which significantly accelerates the inference process.
  arXiv  Detail & Related papers  (2022-10-17T06:00:12Z)
• Learning Geometrically Disentangled Representations of Protein Folding Simulations [72.03095377508856]
  This work focuses on learning a generative neural network on a structural ensemble of a drug-target protein. Model tasks involve characterizing the distinct structural fluctuations of the protein bound to various drug molecules. Results show that our geometric learning-based method enjoys both accuracy and efficiency for generating complex structural variations.
  arXiv  Detail & Related papers  (2022-05-20T19:38:00Z)
• 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)

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.