ReviewCurrent opinion in biotechnology2022
Machine learning to navigate fitness landscapes for protein engineering.
Review in Current opinion in biotechnology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 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
60 citing papers in PubMed.
- Liquid-Liquid Phase Separation-Enhanced Multienzyme Catalysis: Mechanisms and Applications.ChemSusChem · 2026Review
- Fitness translocation: improving variant effect prediction with biologically-grounded data augmentation.Bioinformatics (Oxford, England) · 2026Article
- The Fragmented Nature of Biosensor Development: Challenges and Paths to Mitigation.Biosensors · 2026Review
- How far can you go? Extrapolating values of catalytic activity from known protein landscapes in natural and directed evolution.Chemical Society reviews · 2026Review
- SynFit: Synergistic Contrastive Learning for Multi-Objective Protein Fitness Prediction and Optimization.bioRxiv : the preprint server for biology · 2026Article
- Machine Learning-Driven Simulations of the SARS-CoV-2 Fitness Landscape from Deep Mutational Scanning Experiments.Journal of chemical information and modeling · 2026Article
- Rapid directed evolution guided by protein language models and epistatic interactions.Science (New York, N.Y.) · 2026Article
- Harnessing Nature's Algorithm: From Test Tubes to Autonomous In Vivo Evolution.Biotechnology journal · 2026Review
- On learning functions over biological sequence space: relating Gaussian process priors, regularization, and gauge fixing.Journal of mathematical biology · 2026Article
- Sequence Display enables large-scale sequence-activity datasets for rapid protein evolution.Nature biotechnology · 2026Article
- Supervised learning of protein variant effects across large-scale mutagenesis datasets.Protein science : a publication of the Protein Society · 2026Article
- Inference and Visualization of Complex Genotype-Phenotype Maps.Molecular biology and evolution · 2026Article
- LDBT instead of DBTL: combining machine learning and rapid cell-free testing.Nature communications · 2025Article
- Of Revolutions and Roadblocks: The Emerging Role of Machine Learning in Biocatalysis.ACS central science · 2025Review
- ProT-VAE: Protein Transformer Variational AutoEncoder for functional protein design.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- A Review of the Progress in the Microbial Biosynthesis of Prenylated Aromatic Compounds.Molecules (Basel, Switzerland) · 2025Review
- Data-efficient protein mutational effect prediction with weak supervision by molecular simulation and protein language models.Briefings in bioinformatics · 2025Article
- Inference and visualization of complex genotype-phenotype maps withbioRxiv : the preprint server for biology · 2025Article
- Learning sequence-function relationships with scalable, interpretable Gaussian processes.bioRxiv : the preprint server for biology · 2025Article
- Protein Structure-Function Relationship: A Kernel-PCA Approach for Reaction Coordinate Identification.Journal of chemical theory and computation · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Machine learning (ML) is revolutionizing our ability to understand and predict the complex relationships between protein sequence, structure, and function. Predictive sequence-function models are enabling protein engineers to efficiently search the sequence space for useful proteins with broad applications in biotechnology. In this review, we highlight the recent advances in applying ML to protein engineering. We discuss supervised learning methods that infer the sequence-function mapping from experimental data and new sequence representation strategies for data-efficient modeling. We then describe the various ways in which ML can be incorporated into protein engineering workflows, including purely in silico searches, ML-assisted directed evolution, and generative models that can learn the underlying distribution of the protein function in a sequence space. ML-driven protein engineering will become increasingly powerful with continued advances in high-throughput data generation, data science, and deep learning.
Indexed as
Identifiers
What OpenQuestion holds
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.