ArticleJournal of molecular biology2014
Systematic exploration of ubiquitin sequence, E1 activation efficiency, and experimental fitness in yeast.
Article in Journal of molecular biology, 2014. 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.
- SynFit: Synergistic Contrastive Learning for Multi-Objective Protein Fitness Prediction and Optimization.bioRxiv : the preprint server for biology · 2026Article
- Integrating protein language models and automatic biofoundry for enhanced protein evolution.Nature communications · 2025Article
- MMRT: MultiMut Recursive Tree for predicting functional effects of high-order protein variants from low-order variants.Computational and structural biotechnology journal · 2025Article
- Zero-shot prediction of mutation effects with multimodal deep representation learning guides protein engineering.Cell research · 2024Article
- Unsupervised evolution of protein and antibody complexes with a structure-informed language model.Science (New York, N.Y.) · 2024Article
- Functional effects of mutations in proteins can be predicted and interpreted by guided selection of sequence covariation information.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Inverse folding of protein complexes with a structure-informed language model enables unsupervised antibody evolution.bioRxiv : the preprint server for biology · 2023Article
- ProteinGym: Large-Scale Benchmarks for Protein Design and Fitness Prediction.bioRxiv : the preprint server for biology · 2023Article
- Ensemble Learning with Supervised Methods Based on Large-Scale Protein Language Models for Protein Mutation Effects Prediction.International journal of molecular sciences · 2023Article
- Mutational fitness landscape and drug resistance.Current opinion in structural biology · 2023Review
- Accurate prediction of functional effect of single amino acid variants with deep learning.Computational and structural biotechnology journal · 2023Article
- Integrating deep mutational scanning and low-throughput mutagenesis data to predict the impact of amino acid variants.GigaScience · 2022Article
- AMaLa: Analysis of Directed Evolution Experiments via Annealed Mutational Approximated Landscape.International journal of molecular sciences · 2021Article
- Analyses of HIV proteases variants at the threshold of viability reveals relationships between processing efficiency and fitness.Virus evolution · 2021Article
- Balance between promiscuity and specificity in phage λ host range.The ISME journal · 2021Article
- Exploring amino acid functions in a deep mutational landscape.Molecular systems biology · 2021Article
- Unsupervised Inference of Protein Fitness Landscape from Deep Mutational Scan.Molecular biology and evolution · 2021Article
- Article
- Variant effect predictions capture some aspects of deep mutational scanning experiments.BMC bioinformatics · 2020Article
- MaveDB: an open-source platform to distribute and interpret data from multiplexed assays of variant effect.Genome biology · 2019Article
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.
Funding
Abstract
The complexity of biological interaction networks poses a challenge to understanding the function of individual connections in the overall network. To address this challenge, we developed a high-throughput reverse engineering strategy to analyze how thousands of specific perturbations (encompassing all point mutations in a central gene) impact both a specific edge (interaction to a directly connected node) and an overall network function. We analyzed the effects of ubiquitin mutations on activation by the E1 enzyme and compared these to effects on yeast growth rate. Using this approach, we delineated ubiquitin mutations that selectively impacted the ubiquitin-E1 edge. We find that the elasticity function relating the efficiency of ubiquitin-E1 interaction to growth rate is non-linear and that a greater than 50-fold decrease in E1 activation efficiency is required to reduce growth rate by 2-fold. Despite the robustness of fitness to decreases in E1 activation efficiency, the effects of most ubiquitin mutations on E1 activation paralleled the effects on growth rate. Our observations indicate that most ubiquitin mutations that disrupt E1 activation also disrupt other functions. The structurally characterized ubiquitin-E1 interface encompasses the interfaces of ubiquitin with most other known binding partners, and we propose that this enables E1 in wild-type cells to selectively activate ubiquitin protein molecules capable of binding to other partners from the cytoplasmic pool of ubiquitin protein that will include molecules with chemical damage and/or errors from transcription and translation.
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.