Evidence map›Paper›PMID 24155158›Full record

ArticleProteins2014

Blind prediction of interfacial water positions in CAPRI.

Marc F Lensink, Iain H Moal, Paul A Bates, Panagiotis L Kastritis, Adrien S J Melquiond, Ezgi Karaca, Christophe Schmitz, Marc van Dijk, Alexandre M J J Bonvin, Miriam Eisenstein and 48 more

Abstract read
In one paragraph

Article in Proteins, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing 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

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.

3 · Its place in the literature

Who cites it

24 citing papers in PubMed.

  1. Blind prediction of complex water and ion ensembles around RNA in CASP16.bioRxiv : the preprint server for biology · 2025
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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

58 authors.

Marc F LensinkInterdisciplinary Research Institute USR3078 CNRS, University Lille North of France, Villeneuve d'Ascq, France.
Iain H Moal
Paul A Bates
Panagiotis L Kastritis
Adrien S J Melquiond
Ezgi Karaca
Christophe Schmitz
Marc van Dijk
Alexandre M J J Bonvin
Miriam Eisenstein
Brian Jiménez-García
Solène Grosdidier
Albert Solernou
Laura Pérez-Cano
Chiara Pallara
Juan Fernández-Recio
Jianqing Xu
Pravin Muthu
Krishna Praneeth Kilambi
Jeffrey J Gray
Sergei Grudinin
Georgy Derevyanko
Julie C Mitchell
John Wieting
Eiji Kanamori
Yuko Tsuchiya
Yoichi Murakami
Joy Sarmiento
Daron M Standley
Matsuyuki Shirota
Kengo Kinoshita
Haruki Nakamura
Matthieu Chavent
David W Ritchie
Hahnbeom Park
Junsu Ko
Hasup Lee
Chaok Seok
Yang Shen
Dima Kozakov
Sandor Vajda
Petras J Kundrotas
Ilya A Vakser
Brian G Pierce
Howook Hwang
Thom Vreven
Zhiping Weng
Idit Buch
Efrat Farkash
Haim J Wolfson
Martin Zacharias
Sanbo Qin
Huan-Xiang Zhou
Shen-You Huang
Xiaoqin Zou
Justyna A Wojdyla
Colin Kleanthous
Shoshana J Wodak

Funding

Integrated Resource for Protein Recognition StudiesR01GM074255 · NIGMS · UNIVERSITY OF KANSAS LAWRENCE · PI VAKSER, ILYA · 2005 to 2024
$6.2M
A MULTISTAGE APPROACH TO PROTEIN-PROTEIN DOCKINGR01GM061867 · NIGMS · BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) · PI VAJDA, SANDOR · 2000 to 2016
$4.0M
Prediction of the Structure of Therapeutic Antibodies with their AntigensR01GM078221 · NIGMS · JOHNS HOPKINS UNIVERSITY · PI GRAY, JEFFREY J · 2006 to 2020
$4.0M
Computational Prediction of Protein-Protein DockingK01HG002316 · NHGRI · UNIVERSITY OF WASHINGTON · PI GRAY, JEFFREY J · 2001 to 2005
$686k
Biotechnology and Biological Sciences Research Council GG/G020671/1Cancer Research UK 10748CIHRNHGRI NIH HHS K01 HG002316NIGMS NIH HHS R01 GM061867NIGMS NIH HHS R01 GM074255NIGMS NIH HHS R01GM074255NIGMS NIH HHS R01 GM078221
6 · The paper itself

Abstract

We report the first assessment of blind predictions of water positions at protein-protein interfaces, performed as part of the critical assessment of predicted interactions (CAPRI) community-wide experiment. Groups submitting docking predictions for the complex of the DNase domain of colicin E2 and Im2 immunity protein (CAPRI Target 47), were invited to predict the positions of interfacial water molecules using the method of their choice. The predictions-20 groups submitted a total of 195 models-were assessed by measuring the recall fraction of water-mediated protein contacts. Of the 176 high- or medium-quality docking models-a very good docking performance per se-only 44% had a recall fraction above 0.3, and a mere 6% above 0.5. The actual water positions were in general predicted to an accuracy level no better than 1.5 Å, and even in good models about half of the contacts represented false positives. This notwithstanding, three hotspot interface water positions were quite well predicted, and so was one of the water positions that is believed to stabilize the loop that confers specificity in these complexes. Overall the best interface water predictions was achieved by groups that also produced high-quality docking models, indicating that accurate modelling of the protein portion is a determinant factor. The use of established molecular mechanics force fields, coupled to sampling and optimization procedures also seemed to confer an advantage. Insights gained from this analysis should help improve the prediction of protein-water interactions and their role in stabilizing protein complexes.

Indexed as

Protein Interaction MappingAlgorithmsColicinsComputational BiologyModels, MolecularMolecular Docking SimulationProtein ConformationWatercolicin immunity proteinsColicinsWaterblind predictionCAPRIprotein dockingprotein interfacewater

Identifiers

PMID24155158
PMCPMC4582081

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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.