ArticleCell systems2021
Learning the protein language: Evolution, structure, and function.
Article in Cell systems, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 257 papers, 2 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
257 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Current computational tools for protein lysine acylation site prediction.Briefings in bioinformatics · 2024Pooled it
- Advances in the Application of Protein Language Modeling for Nucleic Acid Protein Binding Site Prediction.Genes · 2024Pooled it
- Protein Language Model Embeddings Reveal Proteome-Scale Ortholog Divergence Relevant to Cross-Species Pharmacology.Proteomes · 2026Article
- xBind: an integrated webserver for large language model-enabled cross-molecular protein binding site prediction.Nucleic acids research · 2026Article
- DeepACPred: an integrated multistage framework for anticancer peptide discovery and activity prediction.Briefings in bioinformatics · 2026Article
- Optimizing protein tokenization: reduced amino acid alphabets for efficient and accurate protein language models.Bioinformatics (Oxford, England) · 2026Article
- The genetic foundations of convergent traits.Nature reviews. Genetics · 2026Review
- Integrating evolutionary and compositional features with ML and DL for robust and interpretable druggable protein prediction.Journal of computer-aided molecular design · 2026Article
- SplitSeek-Pro: accurate prediction of splittable sites on protein structures.Nature communications · 2026Article
- Advances in Protein Function Prediction from the Fifth CAFA Challenge.bioRxiv : the preprint server for biology · 2026Article
- CodonMoE: DNA language models for codon-dependent mRNA prediction.Bioinformatics (Oxford, England) · 2026Article
- ProteomeLM: A proteome-scale language model enables accurate and rapid prediction of protein-protein interactions and gene essentiality across taxa.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- ProSSF: integrating sequence, structure, and gene ontology for prediction of protein stability, interaction, and function.Molecular genetics and genomics : MGG · 2026Article
- A tri-modal contrastive learning framework for protein representation learning.Cell reports methods · 2026Article
- Predicting Peptide Aggregation with Protein Language Model Embeddings.Journal of chemical information and modeling · 2026Article
- Deep Prior Framework: integrating functional specificity with general plausibility for targeted protein evolution.Briefings in bioinformatics · 2026Article
- Protein foundation models: a comprehensive survey.Science China. Life sciences · 2026Review
- Controllable Protein Design by Prefix-Tuning Protein Language Models.Journal of chemical information and modeling · 2026Article
- LiBRe: A Ligand-Aware Sequence-Based Binding Residue Prediction Model for Virtual Screening.Journal of chemical information and modeling · 2026Article
- A DNN Biophysics Model with Topological and Electrostatic Features.Journal of chemical information and modeling · 2026Article
197 more citing papers are in PubMed but not listed here.
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Authors and funding
2 authors.
Funding
Abstract
Language models have recently emerged as a powerful machine-learning approach for distilling information from massive protein sequence databases. From readily available sequence data alone, these models discover evolutionary, structural, and functional organization across protein space. Using language models, we can encode amino-acid sequences into distributed vector representations that capture their structural and functional properties, as well as evaluate the evolutionary fitness of sequence variants. We discuss recent advances in protein language modeling and their applications to downstream protein property prediction problems. We then consider how these models can be enriched with prior biological knowledge and introduce an approach for encoding protein structural knowledge into the learned representations. The knowledge distilled by these models allows us to improve downstream function prediction through transfer learning. Deep protein language models are revolutionizing protein biology. They suggest new ways to approach protein and therapeutic design. However, further developments are needed to encode strong biological priors into protein language models and to increase their accessibility to the broader community.
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Registered trials
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.