ReviewNature reviews. Genetics2022
From systems to structure - using genetic data to model protein structures.
Review in Nature reviews. Genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
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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
14 citing papers in PubMed.
- A structurally informed human protein-protein interactome reveals proteome-wide perturbations caused by disease mutations.Nature biotechnology · 2025Article
- Article
- Ranking Protein-Protein Models with Large Language Models and Graph Neural Networks.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Contextual AI models for single-cell protein biology.Nature methods · 2024Article
- Contextual AI models for single-cell protein biology.bioRxiv : the preprint server for biology · 2024Article
- Structurally-informed human interactome reveals proteome-wide perturbations by disease mutations.bioRxiv : the preprint server for biology · 2024Article
- DeepRank-GNN-esm: a graph neural network for scoring protein-protein models using protein language model.Bioinformatics advances · 2024Article
- Newly discovered variants in unexplained neonatal encephalopathy.Molecular genetics & genomic medicine · 2024Article
- Oligomerization of Monoamine Transporters.Sub-cellular biochemistry · 2024Review
- Computational insight into stability-enhanced systems of anthocyanin with protein/peptide.Food chemistry. Molecular sciences · 2023Article
- Integrative structure determination of histones H3 and H4 using genetic interactions.The FEBS journal · 2023Article
- Mutually beneficial confluence of structure-based modeling of protein dynamics and machine learning methods.Current opinion in structural biology · 2023Review
- Recent Advances in Machine Learning Variant Effect Prediction Tools for Protein Engineering.Industrial & engineering chemistry research · 2022Article
- Knowledge structure and emerging trends in the application of deep learning in genetics research: A bibliometric analysis [2000-2021].Frontiers in genetics · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Understanding the effects of genetic variation is a fundamental problem in biology that requires methods to analyse both physical and functional consequences of sequence changes at systems-wide and mechanistic scales. To achieve a systems view, protein interaction networks map which proteins physically interact, while genetic interaction networks inform on the phenotypic consequences of perturbing these protein interactions. Until recently, understanding the molecular mechanisms that underlie these interactions often required biophysical methods to determine the structures of the proteins involved. The past decade has seen the emergence of new approaches based on coevolution, deep mutational scanning and genome-scale genetic or chemical-genetic interaction mapping that enable modelling of the structures of individual proteins or protein complexes. Here, we review the emerging use of large-scale genetic datasets and deep learning approaches to model protein structures and their interactions, and discuss the integration of structural data from different sources.
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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.