Progressive Multi-Modality Learning for Inverse Protein Folding

• URL: http://arxiv.org/abs/2312.06297v2
• Date: Sat, 20 Jul 2024 12:56:00 GMT
• Title: Progressive Multi-Modality Learning for Inverse Protein Folding
• Authors: Jiangbin Zheng, Stan Z. Li,
• Abstract summary: We propose a novel protein design paradigm called MMDesign, which leverages multi-modality transfer learning. MMDesign is the first framework that combines a pretrained structural module with a pretrained contextual module, using an auto-encoder (AE) based language model to incorporate prior protein semantic knowledge. Experimental results, only training with the small dataset, demonstrate that MMDesign consistently outperforms baselines on various public benchmarks.
• Score: 47.095862120116976
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While deep generative models show promise for learning inverse protein folding directly from data, the lack of publicly available structure-sequence pairings limits their generalization. Previous improvements and data augmentation efforts to overcome this bottleneck have been insufficient. To further address this challenge, we propose a novel protein design paradigm called MMDesign, which leverages multi-modality transfer learning. To our knowledge, MMDesign is the first framework that combines a pretrained structural module with a pretrained contextual module, using an auto-encoder (AE) based language model to incorporate prior protein semantic knowledge. Experimental results, only training with the small dataset, demonstrate that MMDesign consistently outperforms baselines on various public benchmarks. To further assess the biological plausibility, we present systematic quantitative analysis techniques that provide interpretability and reveal more about the laws of 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)
• Structure-Enhanced Protein Instruction Tuning: Towards General-Purpose Protein Understanding [43.811432723460534]
  We introduce Structure-Enhanced Protein Instruction Tuning (SEPIT) framework to bridge this gap. Our approach integrates a noval structure-aware module into pLMs to inform them with structural knowledge, and then connects these enhanced pLMs to large language models (LLMs) to generate understanding of proteins. We construct the largest and most comprehensive protein instruction dataset to date, which allows us to train and evaluate the general-purpose protein understanding model.
  arXiv  Detail & Related papers  (2024-10-04T16:02:50Z)
• ProteinBench: A Holistic Evaluation of Protein Foundation Models [53.59325047872512]
  We introduce ProteinBench, a holistic evaluation framework for protein foundation models. Our approach consists of three key components: (i) A taxonomic classification of tasks that broadly encompass the main challenges in the protein domain, based on the relationships between different protein modalities; (ii) A multi-metric evaluation approach that assesses performance across four key dimensions: quality, novelty, diversity, and robustness; and (iii) In-depth analyses from various user objectives, providing a holistic view of model performance.
  arXiv  Detail & Related papers  (2024-09-10T06:52:33Z)
• 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)
• xTrimoPGLM: Unified 100B-Scale Pre-trained Transformer for Deciphering the Language of Protein [76.18058946124111]
  We propose a unified protein language model, xTrimoPGLM, to address protein understanding and generation tasks simultaneously. xTrimoPGLM significantly outperforms other advanced baselines in 18 protein understanding benchmarks across four categories. It can also generate de novo protein sequences following the principles of natural ones, and can perform programmable generation after supervised fine-tuning.
  arXiv  Detail & Related papers  (2024-01-11T15:03:17Z)
• Target-aware Variational Auto-encoders for Ligand Generation with Multimodal Protein Representation Learning [2.01243755755303]
  We introduce TargetVAE, a target-aware auto-encoder that generates with high binding affinities to arbitrary protein targets. This is the first effort to unify different representations of proteins into a single model that we name as Protein Multimodal Network (PMN)
  arXiv  Detail & Related papers  (2023-08-02T12:08:17Z)
• 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)
• Integration of Pre-trained Protein Language Models into Geometric Deep Learning Networks [68.90692290665648]
  We integrate knowledge learned by protein language models into several state-of-the-art geometric networks. Our findings show an overall improvement of 20% over baselines. Strong evidence indicates that the incorporation of protein language models' knowledge enhances geometric networks' capacity by a significant margin.
  arXiv  Detail & Related papers  (2022-12-07T04:04:04Z)
• Structure-aware Protein Self-supervised Learning [50.04673179816619]
  We propose a novel structure-aware protein self-supervised learning method to capture structural information of proteins. In particular, a well-designed graph neural network (GNN) model is pretrained to preserve the protein structural information. We identify the relation between the sequential information in the protein language model and the structural information in the specially designed GNN model via a novel pseudo bi-level optimization scheme.
  arXiv  Detail & Related papers  (2022-04-06T02:18:41Z)
• Rethinking Relational Encoding in Language Model: Pre-Training for General Sequences [23.806325599416134]
  Language model pre-training fails at modeling per-sequence relations in non-natural language domains. We develop a framework that couples LMPT with deep structure-preserving metric learning to produce richer embeddings. Our approach offers notable performance improvements on downstream tasks.
  arXiv  Detail & Related papers  (2021-03-18T15:51:04Z)

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.