Evidence map›Paper›PMID 40851418›Full record

ArticleProteins2026

Performance of pyDock in 8th CAPRI: Energy-Based Scoring Applied to Docking and AlphaFold Models.

Luis Angel Rodríguez-Lumbreras, Mireia Rosell, Miguel Romero-Durana, Víctor Monteagudo, Fabian Glaser, Juan Fernandez-Recio

Abstract read
In one paragraph

Article in Proteins, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Luis Angel Rodríguez-LumbrerasInstituto de Ciencias de la Vid y del Vino (ICVV), CSIC-Universidad de La Rioja-Gobierno de La Rioja, Logroño, Spain.ORCID https://orcid.org/0000-0002-7502-3157
Mireia RosellInstituto de Ciencias de la Vid y del Vino (ICVV), CSIC-Universidad de La Rioja-Gobierno de La Rioja, Logroño, Spain.
Miguel Romero-DuranaInstituto de Ciencias de la Vid y del Vino (ICVV), CSIC-Universidad de La Rioja-Gobierno de La Rioja, Logroño, Spain.
Víctor MonteagudoInstituto de Ciencias de la Vid y del Vino (ICVV), CSIC-Universidad de La Rioja-Gobierno de La Rioja, Logroño, Spain.
Fabian GlaserTechnion Center for Structural Biology (TCSB) and Technion Human Health Initiative (THHI), Technion-Israel Institute of Technology, Haifa, Israel.
Juan Fernandez-RecioInstituto de Ciencias de la Vid y del Vino (ICVV), CSIC-Universidad de La Rioja-Gobierno de La Rioja, Logroño, Spain.

Funding

Interreg EFA080/01Ministerio de Ciencia e Innovación PID2022-143215OB-I00Next Generation EU Plan de Recuperación, Transformación y Resiliencia
6 · The paper itself

Abstract

The 8th CAPRI edition has shown a significant evolution in the field of protein-protein complex structure prediction. We have participated in all 11 targets proposed in this edition, involving domain-domain, protein-protein, protein-peptide, and protein-DNA interactions, including homo- and hetero-meric interfaces. Our prediction strategy has significantly evolved during this edition due to the appearance of ground-breaking AI-based predicting methodologies, like AlphaFold (AF). As predictors, while for the first targets our modeling approach was mostly based on our standard pyDock protocol, after target T187 and onwards, interacting subunits were routinely modeled by AlphaFold2. In the last round (targets T231-T234) we also generated complex models with AlphaFold-Multimer, which were scored by a combination of pyDock energy and AF model confidence. As scorers, we mostly used pyDock to directly score the provided models, applying the same restraints and additional filters as in predictors. Overall, our performance in this 8th CAPRI edition was in line with that in past editions. Considering successful targets as those with submitted models of acceptable (or better) quality within top 5 (predictors) or top 10 (scorers) for the entire complex or at least for one of their assessment units (AUs), we succeeded in 45% of cases as predictors (ranking 4th among participants) and in 64% of cases as scorers (ranking 3rd among participants). The targets where we failed both as predictors and as scorers were actually challenging for all participants. This experiment has shown that the problem of protein-protein docking is not yet solved, and has confirmed the value of energy-based scoring and other approaches in combination with AI-based predictions.

Indexed as

Computational BiologyMolecular Docking SimulationProteinsSoftwareAlgorithmsBinding SitesDatabases, ProteinDNAHumansPrediction AlgorithmsProtein BindingProtein ConformationProtein FoldingThermodynamicsDNAProteinsAlphaFoldCAPRIcomplex structureprotein–protein dockingpyDock

Identifiers

PMID40851418
PMCPMC13648883

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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.