ArticleScientific reports2017
Exploring the Sequence-based Prediction of Folding Initiation Sites in Proteins.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 28 papers, 1 of them a synthesis that pooled it.
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
28 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Differential proteostatic regulation of insoluble and abundant proteins.Bioinformatics (Oxford, England) · 2019Pooled it
- AbAgKer: a unified semi-supervised framework for antigen-antibody binding affinity and kinetics prediction.Bioinformatics (Oxford, England) · 2026Article
- Interpreting the effects of DNA polymerase variants at the structural level.Molecular oncology · 2026Article
- MAVISp: A modular structure-based framework for protein variant effects.Protein science : a publication of the Protein Society · 2026Article
- In silico identification of archaeal DNA-binding proteins.Bioinformatics (Oxford, England) · 2025Article
- VUS next in rare diseases? Deciphering genetic determinants of biomolecular condensation.Orphanet journal of rare diseases · 2024Review
- bio2Byte Tools deployment as a Python package and Galaxy tool to predict protein biophysical properties.Bioinformatics (Oxford, England) · 2024Article
- Sculpting conducting nanopore size and shape through de novo protein design.Science (New York, N.Y.) · 2024Article
- FoldPAthreader: predicting protein folding pathway using a novel folding force field model derived from known protein universe.Genome biology · 2024Article
- Recent Advances in Protein Folding Pathway Prediction through Computational Methods.Current medicinal chemistry · 2024Review
- Sculpting conducting nanopore size and shape throughbioRxiv : the preprint server for biology · 2023Article
- Evolutionary adaptation of the protein folding pathway for secretability.The EMBO journal · 2022Article
- Decoding an Amino Acid Sequence to Extract Information on Protein Folding.Molecules (Basel, Switzerland) · 2022Review
- Advances in Hydrogen/Deuterium Exchange Mass Spectrometry and the Pursuit of Challenging Biological Systems.Chemical reviews · 2022Review
- Challenges in describing the conformation and dynamics of proteins with ambiguous behavior.Frontiers in molecular biosciences · 2022Article
- Massively parallel interrogation of protein fragment secretability using SECRiFY reveals features influencing secretory system transit.Nature communications · 2021Article
- b2bTools: online predictions for protein biophysical features and their conservation.Nucleic acids research · 2021Article
- In Silico Analysis of the Molecular-Level Impact of SMPD1 Variants on Niemann-Pick Disease Severity.International journal of molecular sciences · 2021Article
- Online biophysical predictions for SARS-CoV-2 proteins.BMC molecular and cell biology · 2021Article
- Interpreting a black box predictor to gain insights into early folding mechanisms.Computational and structural biotechnology journal · 2021Article
Corrections and comments
- Erratum issued
Authors and funding
5 authors.
Funding
Abstract
Protein folding is a complex process that can lead to disease when it fails. Especially poorly understood are the very early stages of protein folding, which are likely defined by intrinsic local interactions between amino acids close to each other in the protein sequence. We here present EFoldMine, a method that predicts, from the primary amino acid sequence of a protein, which amino acids are likely involved in early folding events. The method is based on early folding data from hydrogen deuterium exchange (HDX) data from NMR pulsed labelling experiments, and uses backbone and sidechain dynamics as well as secondary structure propensities as features. The EFoldMine predictions give insights into the folding process, as illustrated by a qualitative comparison with independent experimental observations. Furthermore, on a quantitative proteome scale, the predicted early folding residues tend to become the residues that interact the most in the folded structure, and they are often residues that display evolutionary covariation. The connection of the EFoldMine predictions with both folding pathway data and the folded protein structure suggests that the initial statistical behavior of the protein chain with respect to local structure formation has a lasting effect on its subsequent states.
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.