ArticleComputational and structural biotechnology journal2020
Caretta - A multiple protein structure alignment and feature extraction suite.
Article in Computational and structural biotechnology journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed.
- PARAS: High-Accuracy Machine Learning of Substrate Specificities in Nonribosomal Peptide Synthetases.JACS Au · 2026Article
- Molecular bases of insect odorant receptor function: specificity and evolution.Biological reviews of the Cambridge Philosophical Society · 2026Review
- Faithful Interpretation of Protein Structures through Weighted Persistent Homology Improves Evolutionary Distance Estimation.Molecular biology and evolution · 2025Article
- Article
- Mutations in theInternational journal of molecular sciences · 2024Article
- DCAlign v1.0: aligning biological sequences using co-evolution models and informed priors.Bioinformatics (Oxford, England) · 2023Article
- Abalign: a comprehensive multiple sequence alignment platform for B-cell receptor immune repertoires.Nucleic acids research · 2023Article
- Fast protein structure comparison through effective representation learning with contrastive graph neural networks.PLoS computational biology · 2022Article
- A roadmap for metagenomic enzyme discovery.Natural product reports · 2021Review
- Systematic Structure-Based Search for Ochratoxin-Degrading Enzymes in Proteomes from Filamentous Fungi.Biomolecules · 2021Article
- Bioinformatic analysis of subfamily-specific regions in 3D-structures of homologs to study functional diversity and conformational plasticity in protein superfamilies.Computational and structural biotechnology journal · 2021Article
- Comparative host-coronavirus protein interaction networks reveal pan-viral disease mechanisms.Science (New York, N.Y.) · 2020Article
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The vast number of protein structures currently available opens exciting opportunities for machine learning on proteins, aimed at predicting and understanding functional properties. In particular, in combination with homology modelling, it is now possible to not only use sequence features as input for machine learning, but also structure features. However, in order to do so, robust multiple structure alignments are imperative. Here we present Caretta, a multiple structure alignment suite meant for homologous but sequentially divergent protein families which consistently returns accurate alignments with a higher coverage than current state-of-the-art tools. Caretta is available as a GUI and command-line application and additionally outputs an aligned structure feature matrix for a given set of input structures, which can readily be used in downstream steps for supervised or unsupervised machine learning. We show Caretta's performance on two benchmark datasets, and present an example application of Caretta in predicting the conformational state of cyclin-dependent kinases.
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.