ProteinWeaver: A Divide-and-Assembly Approach for Protein Backbone Design
- URL: http://arxiv.org/abs/2411.16686v2
- Date: Wed, 27 Nov 2024 12:18:46 GMT
- Title: ProteinWeaver: A Divide-and-Assembly Approach for Protein Backbone Design
- Authors: Yiming Ma, Fei Ye, Yi Zhou, Zaixiang Zheng, Dongyu Xue, Quanquan Gu,
- Abstract summary: We introduce ProteinWeaver, a two-stage framework for protein backbone design.
ProteinWeaver generates high-quality, novel protein backbones through versatile domain assembly.
By introducing a divide-and-assembly' paradigm, ProteinWeaver advances protein engineering and opens new avenues for functional protein design.
- Score: 61.19456204667385
- License:
- Abstract: Nature creates diverse proteins through a 'divide and assembly' strategy. Inspired by this idea, we introduce ProteinWeaver, a two-stage framework for protein backbone design. Our method first generates individual protein domains and then employs an SE(3) diffusion model to flexibly assemble these domains. A key challenge lies in the assembling step, given the complex and rugged nature of the inter-domain interaction landscape. To address this challenge, we employ preference alignment to discern complex relationships between structure and interaction landscapes through comparative analysis of generated samples. Comprehensive experiments demonstrate that ProteinWeaver: (1) generates high-quality, novel protein backbones through versatile domain assembly; (2) outperforms RFdiffusion, the current state-of-the-art in backbone design, by 13\% and 39\% for long-chain proteins; (3) shows the potential for cooperative function design through illustrative case studies. To sum up, by introducing a `divide-and-assembly' paradigm, ProteinWeaver advances protein engineering and opens new avenues for functional protein design.
Related papers
- SFM-Protein: Integrative Co-evolutionary Pre-training for Advanced Protein Sequence Representation [97.99658944212675]
We introduce a novel pre-training strategy for protein foundation models.
It emphasizes the interactions among amino acid residues to enhance the extraction of both short-range and long-range co-evolutionary features.
Trained on a large-scale protein sequence dataset, our model demonstrates superior generalization ability.
arXiv Detail & Related papers (2024-10-31T15:22:03Z) - Annotation-guided Protein Design with Multi-Level Domain Alignment [39.79713846491306]
We propose Protein- Alignment Generation, PAAG, a multi-modality protein design framework.
It integrates the textual annotations extracted from protein database for controllable generation in sequence space.
Specifically, PAAG can explicitly generate proteins containing specific domains conditioned on the corresponding domain annotations.
arXiv Detail & Related papers (2024-04-18T09:37:54Z) - Joint Design of Protein Sequence and Structure based on Motifs [11.731131799546489]
We propose GeoPro, a method to design protein backbone structure and sequence jointly.
GeoPro is powered by an equivariant encoder for three-dimensional (3D) backbone structure and a protein sequence decoder guided by 3D geometry.
Our method discovers novel $beta$-lactamases and myoglobins which are not present in protein data bank (PDB) and UniProt.
arXiv Detail & Related papers (2023-10-04T03:07:03Z) - A Latent Diffusion Model for Protein Structure Generation [50.74232632854264]
We propose a latent diffusion model that can reduce the complexity of protein modeling.
We show that our method can effectively generate novel protein backbone structures with high designability and efficiency.
arXiv Detail & Related papers (2023-05-06T19:10:19Z) - A Text-guided Protein Design Framework [106.79061950107922]
We propose ProteinDT, a multi-modal framework that leverages textual descriptions for protein design.
ProteinDT consists of three subsequent steps: ProteinCLAP which aligns the representation of two modalities, a facilitator that generates the protein representation from the text modality, and a decoder that creates the protein sequences from the representation.
We quantitatively verify the effectiveness of ProteinDT on three challenging tasks: (1) over 90% accuracy for text-guided protein generation; (2) best hit ratio on 12 zero-shot text-guided protein editing tasks; (3) superior performance on four out of six protein property prediction benchmarks.
arXiv Detail & Related papers (2023-02-09T12:59:16Z) - 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) - Generative De Novo Protein Design with Global Context [36.21545615114117]
The inverse of protein structure prediction aims to obtain a novel protein sequence that will fold into the defined structure.
Recent works on computational protein design have studied designing sequences for the desired backbone structure with local positional information.
We propose the Global-Context Aware generative de novo protein design method (GCA), consisting of local and global modules.
arXiv Detail & Related papers (2022-04-21T02:55:01Z) - Independent SE(3)-Equivariant Models for End-to-End Rigid Protein
Docking [57.2037357017652]
We tackle rigid body protein-protein docking, i.e., computationally predicting the 3D structure of a protein-protein complex from the individual unbound structures.
We design a novel pairwise-independent SE(3)-equivariant graph matching network to predict the rotation and translation to place one of the proteins at the right docked position.
Our model, named EquiDock, approximates the binding pockets and predicts the docking poses using keypoint matching and alignment.
arXiv Detail & Related papers (2021-11-15T18:46:37Z) - BERTology Meets Biology: Interpreting Attention in Protein Language
Models [124.8966298974842]
We demonstrate methods for analyzing protein Transformer models through the lens of attention.
We show that attention captures the folding structure of proteins, connecting amino acids that are far apart in the underlying sequence, but spatially close in the three-dimensional structure.
We also present a three-dimensional visualization of the interaction between attention and protein structure.
arXiv Detail & Related papers (2020-06-26T21:50:17Z)
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.