Ophiuchus: Scalable Modeling of Protein Structures through Hierarchical Coarse-graining SO(3)-Equivariant Autoencoders

• URL: http://arxiv.org/abs/2310.02508v2
• Date: Wed, 27 Dec 2023 00:52:52 GMT
• Title: Ophiuchus: Scalable Modeling of Protein Structures through Hierarchical Coarse-graining SO(3)-Equivariant Autoencoders
• Authors: Allan dos Santos Costa and Ilan Mitnikov and Mario Geiger and Manvitha Ponnapati and Tess Smidt and Joseph Jacobson
• Abstract summary: Three-dimensional native states of natural proteins display recurring and hierarchical patterns. Traditional graph-based modeling of protein structures is often limited to operate within a single fine-grained resolution. We introduce Ophiuchus, an SO(3)-equivariant coarse-graining model that efficiently operates on all-atom protein structures.
• Score: 1.8835495377767553
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Three-dimensional native states of natural proteins display recurring and hierarchical patterns. Yet, traditional graph-based modeling of protein structures is often limited to operate within a single fine-grained resolution, and lacks hourglass neural architectures to learn those high-level building blocks. We narrow this gap by introducing Ophiuchus, an SO(3)-equivariant coarse-graining model that efficiently operates on all-atom protein structures. Our model departs from current approaches that employ graph modeling, instead focusing on local convolutional coarsening to model sequence-motif interactions with efficient time complexity in protein length. We measure the reconstruction capabilities of Ophiuchus across different compression rates, and compare it to existing models. We examine the learned latent space and demonstrate its utility through conformational interpolation. Finally, we leverage denoising diffusion probabilistic models (DDPM) in the latent space to efficiently sample protein structures. Our experiments demonstrate Ophiuchus to be a scalable basis for efficient protein modeling and generation.

Related papers

• Equivariant Graph Neural Operator for Modeling 3D Dynamics [148.98826858078556]
  We propose Equivariant Graph Neural Operator (EGNO) to directly models dynamics as trajectories instead of just next-step prediction. EGNO explicitly learns the temporal evolution of 3D dynamics where we formulate the dynamics as a function over time and learn neural operators to approximate it. Comprehensive experiments in multiple domains, including particle simulations, human motion capture, and molecular dynamics, demonstrate the significantly superior performance of EGNO against existing methods.
  arXiv  Detail & Related papers  (2024-01-19T21:50:32Z)
• Accelerating Inference in Molecular Diffusion Models with Latent Representations of Protein Structure [0.0]
  Diffusion generative models operate directly on 3D molecular structures. We present a novel GNN-based architecture for learning latent representations of molecular structure. Our model achieves comparable performance to one with an all-atom protein representation while exhibiting a 3-fold reduction in inference time.
  arXiv  Detail & Related papers  (2023-11-22T15:32:31Z)
• Geometric Neural Diffusion Processes [55.891428654434634]
  We extend the framework of diffusion models to incorporate a series of geometric priors in infinite-dimension modelling. We show that with these conditions, the generative functional model admits the same symmetry.
  arXiv  Detail & Related papers  (2023-07-11T16:51:38Z)
• Protein Design with Guided Discrete Diffusion [67.06148688398677]
  A popular approach to protein design is to combine a generative model with a discriminative model for conditional sampling. We propose diffusioN Optimized Sampling (NOS), a guidance method for discrete diffusion models. NOS makes it possible to perform design directly in sequence space, circumventing significant limitations of structure-based methods.
  arXiv  Detail & Related papers  (2023-05-31T16:31:24Z)
• 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)
• EigenFold: Generative Protein Structure Prediction with Diffusion Models [10.24107243529341]
  EigenFold is a diffusion generative modeling framework for sampling a distribution of structures from a given protein sequence. On recent CAMEO targets, EigenFold achieves a median TMScore of 0.84, while providing a more comprehensive picture of model uncertainty.
  arXiv  Detail & Related papers  (2023-04-05T02:46:13Z)
• Internal-Coordinate Density Modelling of Protein Structure: Covariance Matters [9.49959422062959]
  We present a new strategy for modelling protein densities in internal coordinates, which uses constraints in 3D space to induce covariance structure between the internal degrees of freedom. We demonstrate that our approach makes it possible to scale density models of internal coordinates to full protein backbones in two settings.
  arXiv  Detail & Related papers  (2023-02-27T12:18:19Z)
• DIFFormer: Scalable (Graph) Transformers Induced by Energy Constrained Diffusion [66.21290235237808]
  We introduce an energy constrained diffusion model which encodes a batch of instances from a dataset into evolutionary states. We provide rigorous theory that implies closed-form optimal estimates for the pairwise diffusion strength among arbitrary instance pairs. Experiments highlight the wide applicability of our model as a general-purpose encoder backbone with superior performance in various tasks.
  arXiv  Detail & Related papers  (2023-01-23T15:18:54Z)
• Protein Structure and Sequence Generation with Equivariant Denoising Diffusion Probabilistic Models [3.5450828190071646]
  An important task in bioengineering is designing proteins with specific 3D structures and chemical properties which enable targeted functions. We introduce a generative model of both protein structure and sequence that can operate at significantly larger scales than previous molecular generative modeling approaches.
  arXiv  Detail & Related papers  (2022-05-26T16:10:09Z)
• 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)
• G-VAE, a Geometric Convolutional VAE for ProteinStructure Generation [41.66010308405784]
  We introduce a joint geometric-neural networks approach for comparing, deforming and generating 3D protein structures. Our method is able to generate plausible structures, different from the structures in the training data.
  arXiv  Detail & Related papers  (2021-06-22T16:52:48Z)

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.