ReviewBriefings in bioinformatics2023
Artificial intelligence-aided protein engineering: from topological data analysis to deep protein language models.
Review in Briefings in bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
27 citing papers in PubMed.
- Machine learning-assisted directed evolution of plant Rubisco.Science advances · 2026Article
- Critical review of artificial intelligence in synthetic biology: DBTL cycle applications, challenges, and design rules for microbial strain engineering.World journal of microbiology & biotechnology · 2026Review
- Graph identification of proteins in tomograms (GRIP-Tomo) 2.0: Topologically aware classification for proteins.Protein science : a publication of the Protein Society · 2026Article
- Microbial lipases: advances in metagenomics and artificial intelligence for enzyme discovery and engineering.Archives of microbiology · 2026Review
- Artificial intelligence driven protein design and sustainable nanomedicine for advanced theranostics.Bioactive materials · 2026Review
- Discovering naturally occurring antifreeze peptides from microbiome by integrating protein language models and molecular dynamics simulation.Journal of materials chemistry. B · 2026Article
- Optimized deep intelligent parameter efficient fine-tuning protein language model system for predicting dephosphorylation site.Journal of molecular modeling · 2026Article
- Disrupted Higher-Order Topology in OCD Brain Networks Revealed by Hodge Laplacian - an ENIGMA Study.bioRxiv : the preprint server for biology · 2026Article
- A review of recent advances in generative artificial intelligence models for biomolecular sciences.Acta pharmaceutica Sinica. B · 2026Review
- A survey of downstream applications of evolutionary scale modeling protein language models.Quantitative biology (Beijing, China) · 2026Review
- PRIMED: predicting DNA binding residues by leveraging pre-trained protein language models.Frontiers in artificial intelligence · 2026Article
- A Review of Topological Data Analysis and Topological Deep Learning in Molecular Sciences.Journal of chemical information and modeling · 2025Review
- And… cut! - how conformational regulation of CRISPR-Cas effectors directs nuclease activity.The Biochemical journal · 2025Review
- Article
- Transcriptomic HEPN1 signatures predict treatment response in low grade glioma.Discover oncology · 2025Article
- Modification and applications of glucose oxidase: optimization strategies and high-throughput screening technologies.World journal of microbiology & biotechnology · 2025Review
- 'Intelligent' proteins.Cellular and molecular life sciences : CMLS · 2025Review
- A review of transformer models in drug discovery and beyond.Journal of pharmaceutical analysis · 2025Review
- Neural network conditioned to produce thermophilic protein sequences can increase thermal stability.Scientific reports · 2025Article
- Review
Corrections and comments
- Update of
Authors and funding
2 authors.
Funding
Abstract
Protein engineering is an emerging field in biotechnology that has the potential to revolutionize various areas, such as antibody design, drug discovery, food security, ecology, and more. However, the mutational space involved is too vast to be handled through experimental means alone. Leveraging accumulative protein databases, machine learning (ML) models, particularly those based on natural language processing (NLP), have considerably expedited protein engineering. Moreover, advances in topological data analysis (TDA) and artificial intelligence-based protein structure prediction, such as AlphaFold2, have made more powerful structure-based ML-assisted protein engineering strategies possible. This review aims to offer a comprehensive, systematic, and indispensable set of methodological components, including TDA and NLP, for protein engineering and to facilitate their future development.
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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.