ArticleIndustrial & engineering chemistry research2022
Recent Advances in Machine Learning Variant Effect Prediction Tools for Protein Engineering.
Article in Industrial & engineering chemistry research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 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
29 citing papers in PubMed, 45 citations in OpenAlex.
- Engineering selective amyloid precursor protein inhibitors by machine learning and deep mutational scanning.Protein science : a publication of the Protein Society · 2026Article
- Fitness translocation: improving variant effect prediction with biologically-grounded data augmentation.Bioinformatics (Oxford, England) · 2026Article
- Compressing the collective knowledge of ESM into a single protein language model.Nature methods · 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
- Single-sequence protein-RNA complex structure prediction by geometric attention-enabled pairing of biological language models.Cell systems · 2025Article
- Review
- Uncovering differential tolerance to deletions versus substitutions with a protein language model.Cell systems · 2025Article
- OrgNet: orientation-gnostic protein stability assessment using convolutional neural networks.Bioinformatics (Oxford, England) · 2025Article
- Guidelines for releasing a variant effect predictor.Genome biology · 2025Article
- EquiRank: Improved protein-protein interface quality estimation using protein language-model-informed equivariant graph neural networks.Computational and structural biotechnology journal · 2025Article
- Enzyme structure correlates with variant effect predictability.Computational and structural biotechnology journal · 2024Article
- A comprehensive overview of recent advances in generative models for antibodies.Computational and structural biotechnology journal · 2024Review
- Deep neural networks for predicting the affinity landscape of protein-protein interactions.iScience · 2024Article
- Pseudo-perplexity in One Fell Swoop for Protein Fitness Estimation.bioRxiv : the preprint server for biology · 2024Article
- Article
- Deep mutational scanning reveals sequence to function constraints for SWEET family transporters.bioRxiv : the preprint server for biology · 2024Article
- Representing mutations for predicting cancer drug response.Bioinformatics (Oxford, England) · 2024Article
- Leveraging machine learning models for peptide-protein interaction prediction.RSC chemical biology · 2024Review
- ProteoMutaMetrics: machine learning approaches for solute carrier family 6 mutation pathogenicity prediction.RSC advances · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Proteins are Nature's molecular machinery and comprise diverse roles while consisting of chemically similar building blocks. In recent years, protein engineering and design have become important research areas, with many applications in the pharmaceutical, energy, and biocatalysis fields, among others-where the aim is to ultimately create a protein given desired structural and functional properties. It is often critical to model the relationship between a protein's sequence, folded structure, and biological function to assist in such protein engineering pursuits. However, significant challenges remain in concretely mapping an amino acid sequence to specific protein properties and biological activities. Mutations may enhance or diminish molecular protein function, and the epistatic interactions between mutations result in an inherently complex mapping between genetic modifications and protein function. Therefore, estimating the quantitative effects of mutations on protein function(s) remains a grand challenge of biology, bioinformatics, and many related fields and would rapidly accelerate protein engineering tasks when successful. Such estimation is often known as variant effect prediction (VEP). However, progress has been demonstrated in recent years with the development of machine learning (ML) methods in modeling the relationship between mutations and protein function. In this Review, recent advances in variant effect prediction (VEP) are discussed as tools for protein engineering, focusing on techniques incorporating gains from the broader ML community and challenges in estimating biomolecular functional differences. Primary developments highlighted include convolutional neural networks, graph neural networks, and natural language embeddings for protein sequences.
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.