SeqProFT: Applying LoRA Finetuning for Sequence-only Protein Property Predictions

• URL: http://arxiv.org/abs/2411.11530v1
• Date: Mon, 18 Nov 2024 12:40:39 GMT
• Title: SeqProFT: Applying LoRA Finetuning for Sequence-only Protein Property Predictions
• Authors: Shuo Zhang, Jian K. Liu,
• Abstract summary: This study employs the LoRA method to perform end-to-end fine-tuning of the ESM-2 model. A multi-head attention mechanism is integrated into the downstream network to combine sequence features with contact map information.
• Score: 8.112057136324431
• License:
• Abstract: Protein language models (PLMs) are capable of learning the relationships between protein sequences and functions by treating amino acid sequences as textual data in a self-supervised manner. However, fine-tuning these models typically demands substantial computational resources and time, with results that may not always be optimized for specific tasks. To overcome these challenges, this study employs the LoRA method to perform end-to-end fine-tuning of the ESM-2 model specifically for protein property prediction tasks, utilizing only sequence information. Additionally, a multi-head attention mechanism is integrated into the downstream network to combine sequence features with contact map information, thereby enhancing the model's comprehension of protein sequences. Experimental results of extensive classification and regression tasks demonstrate that the fine-tuned model achieves strong performance and faster convergence across multiple regression and classification tasks.

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)
• 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)
• Steering Masked Discrete Diffusion Models via Discrete Denoising Posterior Prediction [88.65168366064061]
  We introduce Discrete Denoising Posterior Prediction (DDPP), a novel framework that casts the task of steering pre-trained MDMs as a problem of probabilistic inference. Our framework leads to a family of three novel objectives that are all simulation-free, and thus scalable. We substantiate our designs via wet-lab validation, where we observe transient expression of reward-optimized protein sequences.
  arXiv  Detail & Related papers  (2024-10-10T17:18:30Z)
• DeepGATGO: A Hierarchical Pretraining-Based Graph-Attention Model for Automatic Protein Function Prediction [4.608328575930055]
  Automatic protein function prediction (AFP) is classified as a large-scale multi-label classification problem. Currently, popular methods primarily combine protein-related information and Gene Ontology (GO) terms to generate final functional predictions. We propose a sequence-based hierarchical prediction method, DeepGATGO, which processes protein sequences and GO term labels hierarchically.
  arXiv  Detail & Related papers  (2023-07-24T07:01:32Z)
• Retrieved Sequence Augmentation for Protein Representation Learning [40.13920287967866]
  We introduce Retrieved Sequence Augmentation for protein representation learning without additional alignment or pre-processing. We show that our model can transfer to new protein domains better and outperforms MSA Transformer on de novo protein prediction. Our study fills a much-encountered gap in protein prediction and brings us a step closer to demystifying the domain knowledge needed to understand protein sequences.
  arXiv  Detail & Related papers  (2023-02-24T10:31:45Z)
• Reprogramming Pretrained Language Models for Protein Sequence Representation Learning [68.75392232599654]
  We propose Representation Learning via Dictionary Learning (R2DL), an end-to-end representation learning framework. R2DL reprograms a pretrained English language model to learn the embeddings of protein sequences. Our model can attain better accuracy and significantly improve the data efficiency by up to $105$ times over the baselines set by pretrained and standard supervised methods.
  arXiv  Detail & Related papers  (2023-01-05T15:55:18Z)
• Pre-training Co-evolutionary Protein Representation via A Pairwise Masked Language Model [93.9943278892735]
  Key problem in protein sequence representation learning is to capture the co-evolutionary information reflected by the inter-residue co-variation in the sequences. We propose a novel method to capture this information directly by pre-training via a dedicated language model, i.e., Pairwise Masked Language Model (PMLM) Our result shows that the proposed method can effectively capture the interresidue correlations and improves the performance of contact prediction by up to 9% compared to the baseline.
  arXiv  Detail & Related papers  (2021-10-29T04:01:32Z)
• Profile Prediction: An Alignment-Based Pre-Training Task for Protein Sequence Models [11.483725773928382]
  Recent deep-learning approaches to protein prediction have shown that pre-training on unlabeled data can yield useful representations for downstream tasks. We introduce a new pre-training task: directly predicting protein profiles derived from multiple sequence alignments. Our results suggest that protein sequence models may benefit from leveraging biologically-inspired inductive biases.
  arXiv  Detail & Related papers  (2020-12-01T01:01:34Z)
• Interpretable Structured Learning with Sparse Gated Sequence Encoder for Protein-Protein Interaction Prediction [2.9488233765621295]
  Predicting protein-protein interactions (PPIs) by learning informative representations from amino acid sequences is a challenging yet important problem in biology. We present a novel deep framework to model and predict PPIs from sequence alone. Our model incorporates a bidirectional gated recurrent unit to learn sequence representations by leveraging contextualized and sequential information from sequences.
  arXiv  Detail & Related papers  (2020-10-16T17:13:32Z)
• A Trainable Optimal Transport Embedding for Feature Aggregation and its Relationship to Attention [96.77554122595578]
  We introduce a parametrized representation of fixed size, which embeds and then aggregates elements from a given input set according to the optimal transport plan between the set and a trainable reference. Our approach scales to large datasets and allows end-to-end training of the reference, while also providing a simple unsupervised learning mechanism with small computational cost.
  arXiv  Detail & Related papers  (2020-06-22T08:35:58Z)

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.