ReviewCurrent opinion in structural biology2023
Using machine learning to predict the effects and consequences of mutations in proteins.
Review in Current opinion in structural biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 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
31 citing papers in PubMed, 56 citations in OpenAlex.
- Harnessing the central dogma: Enzyme-driven gene diversification strategies for in cellulo continuous directed evolution.Fundamental research · 2026Review
- Computational identification of rare pathogenic genomic variants in esophageal cancer markers: Transcript-level analysis, sequence-based insights, and structural-functional impacts of non-synonymous SNPs.Biochemistry and biophysics reports · 2026Article
- Medium-sized protein language models perform well at transfer learning on realistic datasets.Scientific reports · 2025Article
- Medium-sized protein language models perform well at transfer learning on realistic datasets.bioRxiv : the preprint server for biology · 2025Article
- A systematic evaluation of the language-of-viral-escape model using multiple machine learning frameworks.Journal of the Royal Society, Interface · 2025Article
- Decoding the effects of mutation on protein interactions using machine learning.Biophysics reviews · 2025Review
- QAFI: a novel method for quantitative estimation of missense variant impact using protein-specific predictors and ensemble learning.Human genetics · 2025Article
- Evolutionary rewiring of the dynamic network underpinning allosteric epistasis in NS1 of the influenza A virus.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Self-supervised machine learning methods for protein design improve sampling but not the identification of high-fitness variants.Science advances · 2025Article
- Deep Learning Approaches for the Prediction of Protein Functional Sites.Molecules (Basel, Switzerland) · 2025Review
- Language Modelling Techniques for Analysing the Impact of Human Genetic Variation.Bioinformatics and biology insights · 2025Review
- Machine Learning Guided Rational Design of a Non-Heme Iron-Based Lysine Dioxygenase Improves its Total Turnover Number.Chembiochem : a European journal of chemical biology · 2024Article
- Article
- A comprehensive overview of recent advances in generative models for antibodies.Computational and structural biotechnology journal · 2024Review
- SSEmb: A joint embedding of protein sequence and structure enables robust variant effect predictions.Nature communications · 2024Article
- The origin of mutational epistasis.European biophysics journal : EBJ · 2024Article
- Semantic search using protein large language models detects class II microcins in bacterial genomes.mSystems · 2024Article
- Deciphering GB1's Single Mutational Landscape: Insights from MuMi Analysis.The journal of physical chemistry. B · 2024Article
- Analysis of proteins in the light of mutations.European biophysics journal : EBJ · 2024Review
- Stability Oracle: a structure-based graph-transformer framework for identifying stabilizing mutations.Nature communications · 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
4 authors at 2 institutions in 1 country.
Funding
Abstract
Machine and deep learning approaches can leverage the increasingly available massive datasets of protein sequences, structures, and mutational effects to predict variants with improved fitness. Many different approaches are being developed, but systematic benchmarking studies indicate that even though the specifics of the machine learning algorithms matter, the more important constraint comes from the data availability and quality utilized during training. In cases where little experimental data are available, unsupervised and self-supervised pre-training with generic protein datasets can still perform well after subsequent refinement via hybrid or transfer learning approaches. Overall, recent progress in this field has been staggering, and machine learning approaches will likely play a major role in future breakthroughs in protein biochemistry and engineering.
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.