A protein-protein docking procedure traditionally consists in two successive
                    tasks: a search algorithm generates a large number of candidate conformations
                    mimicking the complex existing in vivo between two proteins,
                    and a scoring function is used to rank them in order to extract a native-like
                    one. We have already shown that using Voronoi constructions and a well chosen
                    set of parameters, an accurate scoring function could be designed and optimized.
                    However to be able to perform large-scale in silico exploration
                    of the interactome, a near-native solution has to be found in the ten
                    best-ranked solutions. This cannot yet be guaranteed by any of the existing
                    scoring functions

A Murzin

Anne Poupon

B Schölkopf

CM Sanderson

DW Ritchie

H Berman

I Halperin

Iddo Friedberg

IHW Eibe Frank

J Bernauer

J Janin

J Pontius

JD Boissonnat

JR Quinlan

Julie Bernauer

Jérôme Azé

M Hall

MF Lensink

N Andrusier

PL Kastritis

R Chen

R Mendez

R Mosca

S le Cessie

SJ Wodak

T Bourquard

T Joachims

Thomas Bourquard

WW Cohen

X Su

Y Tong

English

PubMed

A protein-protein docking procedure traditionally consists in two successive tasks: a search algorithm generates a large number of candidate conformations mimicking the complex existing in vivo between two proteins, and a scoring function is used to rank them in order to extract a native-like one. We have already shown that using Voronoi constructions and a well chosen set of parameters, an accurate scoring function could be designed and optimized. However to be able to perform large-scale in silico exploration of the interactome, a near-native solution has to be found in the ten best-ranked solutions. This cannot yet be guaranteed by any of the existing scoring functions. In this work, we introduce a new procedure for conformation ranking. We previously developed a set of scoring functions where learning was performed using a genetic algorithm. These functions were used to assign a rank to each possible conformation. We now have a refined rank using different classifiers (decision trees, rules and support vector machines) in a collaborative filtering scheme. The scoring function newly obtained is evaluated using 10 fold cross-validation, and compared to the functions obtained using either genetic algorithms or collaborative filtering taken separately. This new approach was successfully applied to the CAPRI scoring ensembles. We show that for 10 targets out of 12, we are able to find a near-native conformation in the 10 best ranked solutions. Moreover, for 6 of them, the near-native conformation selected is of high accuracy. Finally, we show that this function dramatically enriches the 100 best-ranking conformations in near-native structures

Directory of Open Access Journals

PLoS ONE

A collaborative filtering approach for protein-protein docking scoring functions.

International audienceA protein-protein docking procedure traditionally consists in two successive tasks: a search algorithm generates a large number of candidate conformations mimicking the complex existing in vivo between two proteins, and a scoring function is used to rank them in order to extract a native-like one. We have already shown that using Voronoi constructions and a well chosen set of parameters, an accurate scoring function could be designed and optimized. However to be able to perform large-scale in silico exploration of the interactome, a near-native solution has to be found in the ten best-ranked solutions. This cannot yet be guaranteed by any of the existing scoring functions. In this work, we introduce a new procedure for conformation ranking. We previously developed a set of scoring functions where learning was performed using a genetic algorithm. These functions were used to assign a rank to each possible conformation. We now have a refined rank using different classifiers (decision trees, rules and support vector machines) in a collaborative filtering scheme. The scoring function newly obtained is evaluated using 10 fold cross-validation, and compared to the functions obtained using either genetic algorithms or collaborative filtering taken separately. This new approach was successfully applied to the CAPRI scoring ensembles. We show that for 10 targets out of 12, we are able to find a near-native conformation in the 10 best ranked solutions. Moreover, for 6 of them, the near-native conformation selected is of high accuracy. Finally, we show that this function dramatically enriches the 100 best-ranking conformations in near-native structures

Bourquard, Thomas

Bernauer, Julie

Azé, Jérôme

Poupon, Anne

HAL: Hyper Article en Ligne

HAL-CentraleSupelec

Crossref

A Collaborative Filtering Approach for Protein-Protein Docking Scoring Functions

ProdInra

A collaborative filtering approach for protein-protein docking scoring functions

HAL-Polytechnique

Bourquard Thomas

Bernauer Julie

Azé Jérôme

Poupon Anne

Public Library of Science (PLOS)

A Collaborative Filtering Approach for Protein-Protein Docking
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A protein-protein docking benchmark.

A survey of collaborative filtering techniques.

Advances in kernel methods - support vector learning .

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Assessment of CAPRI predictions in rounds 3-5 shows progress in docking procedures.

Binding of Rac1, Rnd1, and RhoD to a novel Rho GTPase interaction motif destabilizes dimerization of the plexin-B1 effector domain.

C4.5: Programs for Machine Learning.

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Making large-scale support vector machine learning practical.

Poupon A

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Pushing structural information into the yeast interactome by high-throughput protein docking experiments.

Recent progress and future directions in protein-protein docking.

Ridge estimators in logistic regression.

SCOP: a structural classification of proteins database for the investigation of sequences and structures.

Structural basis of macromolecular recognition.

The cartographers toolbox: building bigger and better human protein interaction networks.

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https://inria.hal.science/inria-00625000v1/document

A Collaborative Filtering Approach for Protein-Protein Docking Scoring Functions

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