ArticleCell research2024
Zero-shot prediction of mutation effects with multimodal deep representation learning guides protein engineering.
Article in Cell research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
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- Rhobot-Screen: an integrated robotic platform for functional screening of rhodopsin variants.BMC biology · 2026Article
- Artificial intelligence and automation in enzyme engineering: evolution, advances, and future perspectives.Bioresources and bioprocessing · 2026Review
- LoMuS: low-rank adaptation with sequence multi-representation improves protein stability prediction.Bioinformatics (Oxford, England) · 2026Article
- Artificial intelligence in biologic drug discovery: A review of methodological evolution and therapeutic applications.Acta pharmaceutica Sinica. B · 2026Review
- Overestimating zero-shot fitness prediction: Broad benchmarks mask local failures and practical limitations.bioRxiv : the preprint server for biology · 2026Article
- Heuristic multi-site optimization for protein sequence design using Masked Protein Language Models.PLoS computational biology · 2026Article
- DPLM: Dynamics-aware Protein Language Model via contrastive learning between sequence and molecular dynamics simulation trajectory.bioRxiv : the preprint server for biology · 2026Article
- Protein foundation models: a comprehensive survey.Science China. Life sciences · 2026Review
- Integrating Protein Language Models with Multimodal Embeddings to Accelerate Function Prediction of Uncharacterized Proteins.International journal of molecular sciences · 2026Review
- A century of vitamin E research: The innovative journey from basic biology to synthetic bio-manufacturing.Journal of integrative plant biology · 2026Review
- Miniature and versatile genome regulation TnpB-ωRNA toolkits facilitate cancer immunotherapy.Nature communications · 2026Article
- Exploring the limits of pre-trained embeddings in machine-guided protein design: a case study on predicting AAV vector viability.Scientific reports · 2026Article
- From Gene Knockouts to Genome Remodeling: Large DNA Fragment Deletion Technologies in Plants.Plants (Basel, Switzerland) · 2026Review
- Engineered TnpB genome editors for plants and human cells identified by ribonucleoprotein mutational scanning.Nature biotechnology · 2026Article
- Predicting Protein-Protein Interactions from Machine-Learned Representations.Advances in experimental medicine and biology · 2026Review
- Protein-nucleic acid language model-assisted design of precise and compact adenine base editor.Nature communications · 2025Article
- Advancing CRISPR with deep learning: A comprehensive review of models and databases.Molecular therapy. Nucleic acids · 2025Review
- Best Practices for Machine Learning-Assisted Protein Engineering.Journal of chemical information and modeling · 2025Review
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Authors and funding
20 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Mutations in amino acid sequences can provoke changes in protein function. Accurate and unsupervised prediction of mutation effects is critical in biotechnology and biomedicine, but remains a fundamental challenge. To resolve this challenge, here we present Protein Mutational Effect Predictor (ProMEP), a general and multiple sequence alignment-free method that enables zero-shot prediction of mutation effects. A multimodal deep representation learning model embedded in ProMEP was developed to comprehensively learn both sequence and structure contexts from ~160 million proteins. ProMEP achieves state-of-the-art performance in mutational effect prediction and accomplishes a tremendous improvement in speed, enabling efficient and intelligent protein engineering. Specifically, ProMEP accurately forecasts mutational consequences on the gene-editing enzymes TnpB and TadA, and successfully guides the development of high-performance gene-editing tools with their engineered variants. The gene-editing efficiency of a 5-site mutant of TnpB reaches up to 74.04% (vs 24.66% for the wild type); and the base editing tool developed on the basis of a TadA 15-site mutant (in addition to the A106V/D108N double mutation that renders deoxyadenosine deaminase activity to TadA) exhibits an A-to-G conversion frequency of up to 77.27% (vs 69.80% for ABE8e, a previous TadA-based adenine base editor) with significantly reduced bystander and off-target effects compared to ABE8e. ProMEP not only showcases superior performance in predicting mutational effects on proteins but also demonstrates a great capability to guide protein engineering. Therefore, ProMEP enables efficient exploration of the gigantic protein space and facilitates practical design of proteins, thereby advancing studies in biomedicine and synthetic biology.
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