ArticleBioinformatics (Oxford, England)2020
UDSMProt: universal deep sequence models for protein classification.
Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 68 papers, 1 of them a synthesis that pooled it.
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Who cites it
68 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Advances in the Application of Protein Language Modeling for Nucleic Acid Protein Binding Site Prediction.Genes · 2024Pooled it
- Antimicrobial Peptides Against Antimicrobial-Resistant Bacteria: Focus on Machine Learning.Infection and drug resistance · 2026Review
- EC-Bench: a benchmark for enzyme commission number prediction.Bioinformatics advances · 2026Article
- BridgeNet: a high-efficiency framework integrating sequence and structure for protein and enzyme function prediction.Briefings in bioinformatics · 2025Article
- TXSelect: A multi-task learning model to identify secretory effectors.PLoS computational biology · 2025Article
- Protein language model identifies disordered, conserved motifs implicated in phase separation.eLife · 2025Article
- Autoregressive enzyme function prediction with multi-scale multi-modality fusion.Briefings in bioinformatics · 2025Article
- Beyond digital twins: the role of foundation models in enhancing the interpretability of multiomics modalities in precision medicine.FEBS open bio · 2025Review
- Harnessing deep learning for proteome-scale detection of amyloid signaling motifs.Bioinformatics (Oxford, England) · 2025Article
- RLSuccSite: succinylation sites prediction based on reinforcement learning dynamic with balanced reward mechanism and three-peaks enhanced method for physicochemical property scores.Journal of cheminformatics · 2025Article
- DRBP-EDP: classification of DNA-binding proteins and RNA-binding proteins using ESM-2 and dual-path neural network.NAR genomics and bioinformatics · 2025Article
- Learning maximally spanning representations improves protein function annotation.bioRxiv : the preprint server for biology · 2025Article
- A Benchmarking Platform for Assessing Protein Language Models on Function-Related Prediction Tasks.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Machine and deep learning to predict viral fusion peptides.Computational and structural biotechnology journal · 2025Article
- MCNN_MC: Computational Prediction of Mitochondrial Carriers and Investigation of Bongkrekic Acid Toxicity Using Protein Language Models and Convolutional Neural Networks.Journal of chemical information and modeling · 2024Article
- TAWFN: a deep learning framework for protein function prediction.Bioinformatics (Oxford, England) · 2024Article
- PANDA-3D: protein function prediction based on AlphaFold models.NAR genomics and bioinformatics · 2024Article
- Computational tools for plant genomics and breeding.Science China. Life sciences · 2024Review
- GloEC: a hierarchical-aware global model for predicting enzyme function.Briefings in bioinformatics · 2024Article
- Exploring the sequence-function space of microbial fucosidases.Communications chemistry · 2024Article
8 more citing papers are in PubMed but not listed here.
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationInferring the properties of a protein from its amino acid sequence is one of the key problems in bioinformatics. Most state-of-the-art approaches for protein classification are tailored to single classification tasks and rely on handcrafted features, such as position-specific-scoring matrices from expensive database searches. We argue that this level of performance can be reached or even be surpassed by learning a task-agnostic representation once, using self-supervised language modeling, and transferring it to specific tasks by a simple fine-tuning step.
resultsWe put forward a universal deep sequence model that is pre-trained on unlabeled protein sequences from Swiss-Prot and fine-tuned on protein classification tasks. We apply it to three prototypical tasks, namely enzyme class prediction, gene ontology prediction and remote homology and fold detection. The proposed method performs on par with state-of-the-art algorithms that were tailored to these specific tasks or, for two out of three tasks, even outperforms them. These results stress the possibility of inferring protein properties from the sequence alone and, on more general grounds, the prospects of modern natural language processing methods in omics. Moreover, we illustrate the prospects for explainable machine learning methods in this field by selected case studies. AVAILABILITY AND IMPLEMENTATION: Source code is available under https://github.com/nstrodt/UDSMProt. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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