ArticleCell systems2021
Inferring Protein Sequence-Function Relationships with Large-Scale Positive-Unlabeled Learning.
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 31 papers.
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Who cites it
31 citing papers in PubMed.
- Article
- Yield Prediction of Organic Reactions in Biased Data Sets via Positive-Unlabeled Learning.Journal of the American Chemical Society · 2026Article
- Intrinsic dataset features drive mutational effect prediction by protein language models.bioRxiv : the preprint server for biology · 2026Article
- Overcoming extrapolation challenges of deep learning by incorporating physics in protein sequence-function modeling.PLoS computational biology · 2026Article
- Overcoming Extrapolation Challenges of Deep Learning by Incorporating Physics in Protein Sequence-Function Modeling.bioRxiv : the preprint server for biology · 2025Article
- QAFI: a novel method for quantitative estimation of missense variant impact using protein-specific predictors and ensemble learning.Human genetics · 2025Article
- Repetitive transcranial magnetic stimulation alleviates neuropathic pain via microglial polarization by modulating the METTL3/NMDAR2B/NLRP3 pathway.Frontiers in immunology · 2025Article
- SHARK enables sensitive detection of evolutionary homologs and functional analogs in unalignable and disordered sequences.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Deep neural networks for predicting the affinity landscape of protein-protein interactions.iScience · 2024Article
- Predicting protein functions using positive-unlabeled ranking with ontology-based priors.Bioinformatics (Oxford, England) · 2024Article
- Learning peptide properties with positive examples only.Digital discovery · 2024Article
- Enhancing the endo-activity of the thermophilic chitinase to yield chitooligosaccharides with high degrees of polymerization.Bioresources and bioprocessing · 2024Article
- Is Novelty Predictable?Cold Spring Harbor perspectives in biology · 2024Review
- MBE: model-based enrichment estimation and prediction for differential sequencing data.Genome biology · 2023Article
- Generative models for protein sequence modeling: recent advances and future directions.Briefings in bioinformatics · 2023Review
- Recent Advances in β-Glucosidase Sequence and Structure Engineering: A Brief Review.Molecules (Basel, Switzerland) · 2023Review
- Global epistasis on fitness landscapes.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2023Review
- From Deep Mutational Mapping of Allosteric Protein Landscapes to Deep Learning of Allostery and Hidden Allosteric Sites: Zooming in on "Allosteric Intersection" of Biochemical and Big Data Approaches.International journal of molecular sciences · 2023Review
- Deep mutational scanning: A versatile tool in systematically mapping genotypes to phenotypes.Frontiers in genetics · 2023Review
- Defining the characteristics of interferon-alpha-stimulated human genes: insight from expression data and machine learning.GigaScience · 2022Article
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Authors and funding
5 authors.
Funding
Abstract
Machine learning can infer how protein sequence maps to function without requiring a detailed understanding of the underlying physical or biological mechanisms. It is challenging to apply existing supervised learning frameworks to large-scale experimental data generated by deep mutational scanning (DMS) and related methods. DMS data often contain high-dimensional and correlated sequence variables, experimental sampling error and bias, and the presence of missing data. Notably, most DMS data do not contain examples of negative sequences, making it challenging to directly estimate how sequence affects function. Here, we develop a positive-unlabeled (PU) learning framework to infer sequence-function relationships from large-scale DMS data. Our PU learning method displays excellent predictive performance across ten large-scale sequence-function datasets, representing proteins of different folds, functions, and library types. The estimated parameters pinpoint key residues that dictate protein structure and function. Finally, we apply our statistical sequence-function model to design highly stabilized enzymes.
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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.