scholarly journals Consistent Improvement of Cross-Docking Results Using Binding Site Ensembles Generated with Elastic Network Normal Modes

2009 ◽  
Vol 49 (3) ◽  
pp. 716-725 ◽  
Author(s):  
Manuel Rueda ◽  
Giovanni Bottegoni ◽  
Ruben Abagyan
Keyword(s):  
Binding Site ◽  
Normal Modes ◽  
Elastic Network ◽  
Cross Docking

Correction to On the Applicability of Elastic Network Normal Modes in Small-Molecule Docking

2014 ◽  
Vol 54 (12) ◽  
pp. 3453-3453 ◽  
Author(s):  
Matthias Dietzen ◽  
Elena Zotenko ◽  
Andreas Hildebrandt ◽  
Thomas Lengauer
Keyword(s):  
Small Molecule ◽  
Normal Modes ◽  
Elastic Network ◽  
Molecule Docking

scholarly journals Hidden partners: Using cross-docking calculations to predict binding sites for proteins with multiple interactions

2018 ◽  
Author(s):  
Nathalie Lagarde ◽  
Alessandra Carbone ◽  
Sophie Sacquin-Mora
Keyword(s):  
Nucleic Acid ◽  
Binding Site ◽  
Protein Interactions ◽  
Binding Sites ◽  
Protein Protein Interactions ◽  
Protein Binding Sites ◽  
Cross Docking ◽  
Multiple Binding Sites ◽  
Multiple Binding ◽  
Docking Calculations

AbstractProtein-protein interactions control a large range of biological processes and their identification is essential to understand the underlying biological mechanisms. To complement experimental approaches, in silico methods are available to investigate protein-protein interactions. Cross-docking methods, in particular, can be used to predict protein binding sites. However, proteins can interact with numerous partners and can present multiple binding sites on their surface, which may alter the binding site prediction quality. We evaluate the binding site predictions obtained using complete cross-docking simulations of 358 proteins with two different scoring schemes accounting for multiple binding sites. Despite overall good binding site prediction performances, 68 cases were still associated with very low prediction quality, presenting individual area under the specificity-sensitivity ROC curve (AUC) values below the random AUC threshold of 0.5, since cross-docking calculations can lead to the identification of alternate protein binding sites (that are different from the reference experimental sites). For the large majority of these proteins, we show that the predicted alternate binding sites correspond to interaction sites with hidden partners, i.e. partners not included in the original cross-docking dataset. Among those new partners, we find proteins, but also nucleic acid molecules. Finally, for proteins with multiple binding sites on their surface, we investigated the structural determinants associated with the binding sites the most targeted by the docking partners.AbbreviationsANOVA: ANalysis Of Variance; AUC: Area Under the Curve; Best Interface: BI; CAPRI: Critical Assessment of Prediction of Interactions; CC-D: Complete Cross-Docking; DNA: DesoxyriboNucleic Acid; FDR: False Discovery Rate; FRIres(type): Fraction of each Residue type in the Interface; FP: False Positives; GI: Global Interface; HCMD: Help Cure Muscular Dystrophy; JET: Joint Evolutionary Tree; MAXDo: Molecular Association via Cross Docking; NAI: Nucleic Acid Interface; NPV: Negative Predicted Value; PDB: Protein Data Bank; PIP: Protein Interface Propensity; PiQSi: Protein Quaternary Structure investigation; PPIs: Protein-Protein Interactions; PPV: Positive Predicted Value; Prec.: Precision; PrimI: Primary Interface; RNA: RiboNucleic Acid; ROC: Receiver Operating Characteristic; SecI: Secondary Interface; Sen.: Sensitivity; Spe.: Specificity; TN: True Negatives; TP: True Positives; WCG: World Community Grid.

On the Applicability of Elastic Network Normal Modes in Small-Molecule Docking

2012 ◽  
Vol 52 (3) ◽  
pp. 844-856 ◽  
Author(s):  
Matthias Dietzen ◽  
Elena Zotenko ◽  
Andreas Hildebrandt ◽  
Thomas Lengauer
Keyword(s):  
Small Molecule ◽  
Normal Modes ◽  
Elastic Network ◽  
Molecule Docking

scholarly journals A coarse-grained elastic network atom contact model and its use in the simulation of protein dynamics and the prediction of the effect of mutations

2013 ◽  
Author(s):  
Vincent Frappier ◽  
Rafael Najmanovich
Keyword(s):  
Potential Energy ◽  
Normal Mode Analysis ◽  
Protein Structures ◽  
Normal Modes ◽  
Potential Energy Function ◽  
Contact Model ◽  
Coarse Grained ◽  
Mode Analysis ◽  
Elastic Network Model ◽  
Elastic Network

Normal mode analysis (NMA) methods are widely used to study dynamic aspects of protein structures. Two critical components of NMA methods are coarse-graining in the level of simplification used to represent protein structures and the choice of potential energy functional form. There is a trade-off between speed and accuracy in different choices. In one extreme one finds accurate but slow molecular-dynamics based methods with all-atom representations and detailed atom potentials. On the other extreme, fast elastic network model (ENM) methods with Cαonly representations and simplified potentials that based on geometry alone, thus oblivious to protein sequence. Here we present ENCoM, an Elastic Network Contact Model that employs a potential energy function that includes a pairwise atom-type non-bonded interaction term and thus makes it possible to consider the effect of the specific nature of amino-acids on dynamics within the context of NMA. ENCoM is as fast as existing ENM methods and outperforms such methods in the generation of conformational ensembles. Here we introduce a new application for NMA methods with the use of ENCoM in the prediction of the effect of mutations on protein stability. While existing methods are based on machine learning or enthalpic considerations, the use of ENCoM, based on vibrational normal modes, is based on entropic considerations. This represents a novel area of application for NMA methods and a novel approach for the prediction of the effect of mutations. We compare ENCoM to a large number of methods in terms of accuracy and self-consistency. We show that the accuracy of ENCoM is comparable to that of the best existing methods. We show that existing methods are biased towards the prediction of destabilizing mutations and that ENCoM is less biased at predicting stabilizing mutations.

scholarly journals Elastic network normal modes provide a basis for protein structure refinement

2012 ◽  
Vol 136 (19) ◽  
pp. 195101 ◽  
Author(s):  
Pawel Gniewek ◽  
Andrzej Kolinski ◽  
Robert L. Jernigan ◽  
Andrzej Kloczkowski
Keyword(s):  
Protein Structure ◽  
Structure Refinement ◽  
Normal Modes ◽  
Elastic Network ◽  
Protein Structure Refinement
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