ArticleNature methods2019
Unified rational protein engineering with sequence-based deep representation learning.
Article in Nature methods, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 503 papers, 1 of them a synthesis that pooled it.
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Who cites it
503 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
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- Engineering peroxygenases for pharmaceutical applications: recent advances and future perspectives.Chemical science · 2026Review
- Computational design and translational development of TCR-mimic scFv antibodies for peptide-HLA neoantigen targeting.npj drug discovery · 2026Review
- Hierarchical Organization of Evolutionary Constraint in Duplicated EF-Hand Motifs.Journal of molecular evolution · 2026Article
- Generative AI designs functional thiolation domains for reprogramming non-ribosomal peptide synthetases.Nature communications · 2026Article
- FunVFPred: Predicting fungal virulence factors using a unified representation learning model.iScience · 2026Article
- Functional alignment of protein language models via reinforcement learning.Nature communications · 2026Article
- Leveraging latent space models for enzyme discovery and sampling.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- FANTASIA suite: a reproducible and configurable framework for embedding-based functional annotation of proteins.NAR genomics and bioinformatics · 2026Article
- iBitter-HF: A Method for Bitter Peptide Sequence Identification Based on Hybrid Feature Embedding.Foods (Basel, Switzerland) · 2026Article
- Prioritization of deleterious mutations informs genomic prediction and increases the rate of gain in common bean (Phaseolus vulgaris L.), a simulation study.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Rank-guided learning accelerates automated enzyme engineering.Nature communications · 2026Article
- Discovery and protein language model-guided design of hyperactive transposases.Nature biotechnology · 2026Article
- Multi-objective antibody design and optimization using machine learning.Nature reviews bioengineering · 2026Article
- Artificial Intelligence and Protein Design: A retrospective study on 20-year emerging trends and core research areas from bibliometric perspectives.Probiotics and antimicrobial proteins · 2026Article
- Advancing bioinformatics with language models: components, applications, and perspectives.Briefings in bioinformatics · 2026Review
- LoMuS: low-rank adaptation with sequence multi-representation improves protein stability prediction.Bioinformatics (Oxford, England) · 2026Article
- Fitness translocation: improving variant effect prediction with biologically-grounded data augmentation.Bioinformatics (Oxford, England) · 2026Article
- Optimizing protein tokenization: reduced amino acid alphabets for efficient and accurate protein language models.Bioinformatics (Oxford, England) · 2026Article
443 more citing papers are in PubMed but not listed here.
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Authors and funding
5 authors.
Funding
Abstract
Rational protein engineering requires a holistic understanding of protein function. Here, we apply deep learning to unlabeled amino-acid sequences to distill the fundamental features of a protein into a statistical representation that is semantically rich and structurally, evolutionarily and biophysically grounded. We show that the simplest models built on top of this unified representation (UniRep) are broadly applicable and generalize to unseen regions of sequence space. Our data-driven approach predicts the stability of natural and de novo designed proteins, and the quantitative function of molecularly diverse mutants, competitively with the state-of-the-art methods. UniRep further enables two orders of magnitude efficiency improvement in a protein engineering task. UniRep is a versatile summary of fundamental protein features that can be applied across protein engineering informatics.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.