Characterizing the fragmentation of AlphaFold predictions.

Abstract

Motivation

The Nobel Prize-winning program AlphaFold2 computes plausible structures of (well) folded proteins. The main quality assessment is based on the predicted local distance difference test (pLDDT), a per-amino acid confidence score.Results

To enhance quality assessment, we provide novel quantitative measures to identify coherent amino acid (a.a.) stretches along the sequence in terms of pLDDT values. These constructions, grounded in standard techniques from topological data analysis and combinatorics, provide a canonical framework for identifying regions along the protein backbone and analyzing their properties, such as their propensity for disorder and their consistency with a null model. The outcome of our analysis can readily be used to select reliable regions/domains within proteins whose pLDDT values span the entire pLDDT range.Results

Availability and implementation: The software acompanying this work is available in the package Alphafold_analysis of the Structural Bioinformatics Library.

Combining AlphaFold 3 with microscale thermophoresis for aptamer optimisation
A. Harkai, T. Meszaros
bioRxiv8 Oct 2026PreprintCC BYFull textMolecular biology & gene regulationbioRxiv
When can AlphaFold predict the oligomeric states of proteins?
Yiechang Lin, Ciara Wallis, Ben Corry
Protein Science16 Jun 2026Peer-reviewedCC BY9 citedMolecular biology & gene regulationOpenAlex
Calibrating AlphaFold confidence scores for interface quality
Sagyndyk Sagadinov, Vsevolod Viliuga, Arne Elofsson
arXiv12 Oct 2026PreprintCC BY 4.0Full textBiophysicsarXiv
Residue-Level Energetic Analysis of Transient Misfolding in the Whirlin PDZ Tandem Reveals a Possible Connection to an AlphaFold-Predicted Alternative Topology.
Valeria Pennacchietti, Mariana Di Felice, Julian Toso +5
International journal of molecular sciences24 Sep 2026Peer-reviewedCC BYFull textMolecular biology & gene regulationEurope PMC
HInt: interaction-based homology discovery through accelerated genome-scale AlphaFold screening
Quentin Rouger, Pierre Paillard, Manon Thomet +5
HAL5 Aug 2026PreprintCC BY-NC-NDFull text1 citedMolecular biology & gene regulationHAL

Cite

BibTeX RIS

BibTeX

@article{cazals2026characterizing,
  title = {{Characterizing the fragmentation of AlphaFold predictions.}},
  author = {Frédéric Cazals and Edoardo Sarti},
  journal = {Bioinformatics advances},
  year = {2026},
  month = oct,
  doi = {10.1093/bioadv/vbag186},
  url = {https://europepmc.org/article/MED/42921180},
  note = {CC BY},
}

RIS

TY  - JOUR
TI  - Characterizing the fragmentation of AlphaFold predictions.
AU  - Frédéric Cazals
AU  - Edoardo Sarti
PY  - 2026
DA  - 2026/10/23
JO  - Bioinformatics advances
DO  - 10.1093/bioadv/vbag186
UR  - https://europepmc.org/article/MED/42921180
AB  - Motivation  The Nobel Prize-winning program AlphaFold2 computes plausible structures of (well) folded proteins. The main quality assessment is based on the predicted local distance difference test (pLDDT), a per-amino acid confidence score.Results  To enhance quality assessment, we provide novel quantitative measures to identify coherent amino acid (a.a.) stretches along the sequence in terms of pLDDT values. These constructions, grounded in standard techniques from topological data analysis and combinatorics, provide a canonical framework for identifying regions along the protein backbone and analyzing their properties, such as their propensity for disorder and their consistency with a null model. The outcome of our analysis can readily be used to select reliable regions/domains within proteins whose pLDDT values span the entire pLDDT range.Results  Availability and implementation: The software acompanying this work is available in the package Alphafold_analysis of the Structural Bioinformatics Library.
SN  - 2635-0041
ER  -

Abstract

Related

Cite