SurfPro: Functional Protein Design Based on Continuous Surface

• URL: http://arxiv.org/abs/2405.06693v2
• Date: Mon, 17 Jun 2024 20:20:58 GMT
• Title: SurfPro: Functional Protein Design Based on Continuous Surface
• Abstract summary: SurfPro is a new method to generate functional proteins given a desired surface and its associated biochemical properties. We evaluate SurfPro on a standard inverse folding benchmark CATH 4.2 and two functional protein design tasks. Our SurfPro consistently surpasses previous state-of-the-art inverse folding methods, achieving a recovery rate of 57.78% on CATH 4.2.
• Score: 14.199812995207072
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: How can we design proteins with desired functions? We are motivated by a chemical intuition that both geometric structure and biochemical properties are critical to a protein's function. In this paper, we propose SurfPro, a new method to generate functional proteins given a desired surface and its associated biochemical properties. SurfPro comprises a hierarchical encoder that progressively models the geometric shape and biochemical features of a protein surface, and an autoregressive decoder to produce an amino acid sequence. We evaluate SurfPro on a standard inverse folding benchmark CATH 4.2 and two functional protein design tasks: protein binder design and enzyme design. Our SurfPro consistently surpasses previous state-of-the-art inverse folding methods, achieving a recovery rate of 57.78% on CATH 4.2 and higher success rates in terms of protein-protein binding and enzyme-substrate interaction scores.

Related papers

• SurfDesign: Effective Protein Design on Molecular Surfaces [75.87423747412242]
  We introduce SurfDesign, a surface-conditioned protein design framework.<n>SurfDesign employs surface-based equivariant message passing to capture surface normals, curvature, and directional geometry.<n>We show that SurfDesign consistently outperforms prior surface-conditioned and backbone-only methods on de novo binder and enzyme design benchmarks.
  arXiv  Detail & Related papers  (2026-05-25T19:53:02Z)
• Surface-based Molecular Design with Multi-modal Flow Matching [64.00572241268597]
  SurfFlow is a novel surface-based generative algorithm that enables comprehensive co-design of sequence, structure, and surface for peptides.<n> evaluated on the comprehensive PepMerge benchmark, SurfFlow consistently outperforms full-atom baselines across all metrics.
  arXiv  Detail & Related papers  (2026-01-08T02:19:29Z)
• Protein Design with Dynamic Protein Vocabulary [22.358650729894443]
  We introduce ProDVa, a novel protein design approach that integrates a text encoder for functional descriptions, a protein language model for designing proteins, and a fragment encoder to dynamically retrieve protein fragments.<n>Compared to state-of-the-art models, ProDVa achieves comparable function alignment using less than 0.04% of the training data, while designing significantly more well-folded proteins.
  arXiv  Detail & Related papers  (2025-05-25T03:50:50Z)
• ProteinWeaver: A Divide-and-Assembly Approach for Protein Backbone Design [61.19456204667385]
  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.
  arXiv  Detail & Related papers  (2024-11-08T08:10:49Z)
• ProLLM: Protein Chain-of-Thoughts Enhanced LLM for Protein-Protein Interaction Prediction [54.132290875513405]
  The prediction of protein-protein interactions (PPIs) is crucial for understanding biological functions and diseases. Previous machine learning approaches to PPI prediction mainly focus on direct physical interactions. We propose a novel framework ProLLM that employs an LLM tailored for PPI for the first time.
  arXiv  Detail & Related papers  (2024-03-30T05:32:42Z)
• NaNa and MiGu: Semantic Data Augmentation Techniques to Enhance Protein Classification in Graph Neural Networks [60.48306899271866]
  We propose novel semantic data augmentation methods to incorporate backbone chemical and side-chain biophysical information into protein classification tasks. Specifically, we leverage molecular biophysical, secondary structure, chemical bonds, andionic features of proteins to facilitate classification tasks.
  arXiv  Detail & Related papers  (2024-03-21T13:27:57Z)
• An approach to solve the coarse-grained Protein folding problem in a Quantum Computer [0.0]
  Understanding protein structures and enzymes plays a critical role in target based drug designing, elucidating protein-related disease mechanisms, and innovating novel enzymes. Recent advancements in AI based protein structure prediction methods have solved the protein folding problem to an extent, but their precision in determining the structure of the protein with low sequence similarity is limited. In this work we developed a novel turn based encoding algorithm that can be run on a gate based quantum computer for predicting the structure of smaller protein sequences.
  arXiv  Detail & Related papers  (2023-11-23T18:20:05Z)
• 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)
• 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)
• 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)
• 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)
• Leveraging Sequence Embedding and Convolutional Neural Network for Protein Function Prediction [27.212743275697825]
  Main challenges of protein function prediction are the large label space and the lack of labeled training data. Our method leverages unsupervised sequence embedding and the success of deep convolutional neural network to overcome these challenges.
  arXiv  Detail & Related papers  (2021-12-01T08:31:01Z)
• 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)
• Lattice protein design using Bayesian learning [0.0]
  Protein design is the inverse approach of the three-dimensional (3D) structure prediction for elucidating the relationship between the 3D structures and amino acid sequences. Here, we propose a novel statistical mechanical design method using Bayesian learning, which can design lattice proteins without the exhaustive conformational search. We find a strong linearity between the chemical potential of water and the number of surface residues, thereby revealing the relationship between protein structure and the effect of water molecules.
  arXiv  Detail & Related papers  (2020-03-14T10:27:40Z)

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.