Fully Blind Docking at the Atomic Level for Protein-Peptide Complex Structure Prediction

Yan, Chengfei; Xu, Xianjin; Zou, Xiaoqin

doi:10.1016/j.str.2016.07.021

Cited by 87 publications

(98 citation statements)

References 49 publications

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“…On a more general term, our experience in CAPRI rounds 28 and 29 highlighted the need for a better protocol to address global peptide docking, including possible new directions. Several global docking protocols have recently been developed that are able to identify the binding site and generate models of variable accuracy, including the global docking of pre‐folded peptides, as well as the search for conformations similar to existing peptide‐protein interactions . FlexPepDock can in principle serve as the local refinement step of such global docking methods.…”

Section: Discussionmentioning

confidence: 99%

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FlexPepDock lessons from CAPRI peptide–protein rounds and suggested new criteria for assessment of model quality and utility

et al. 2017

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CAPRI rounds 28 and 29 included, for the first time, peptide-receptor targets of three different systems, reflecting increased appreciation of the importance of peptide-protein interactions. The CAPRI rounds allowed us to objectively assess the performance of Rosetta FlexPepDock, one of the first protocols to explicitly include peptide flexibility in docking, accounting for peptide conformational changes upon binding. We discuss here successes and challenges in modeling these targets: we obtain top-performing, high-resolution models of the peptide motif for cases with known binding sites but there is a need for better modeling of flanking regions, as well as better selection criteria, in particular for unknown binding sites. These rounds have also provided us the opportunity to reassess the success criteria, to better reflect the quality of a peptide-protein complex model. Using all models submitted to CAPRI, we analyze the correlation between current classification criteria and the ability to retrieve critical interface features, such as hydrogen bonds and hotspots. We find that loosening the backbone (and ligand) RMSD threshold, together with a restriction on the side chain RMSD measure, allows us to improve the selection of high-accuracy models. We also suggest a new measure to assess interface hydrogen bond recovery, which is not assessed by the current CAPRI criteria. Finally, we find that surprisingly much can be learned from rather inaccurate models about binding hotspots, suggesting that the current status of peptide-protein docking methods, as reflected by the submitted CAPRI models, can already have a significant impact on our understanding of protein interactions. Proteins 2017; 85:445-462. © 2016 Wiley Periodicals, Inc.

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Section: Discussionmentioning

confidence: 99%

“…Since then, a range of other original and very successful tools for modeling peptide-protein complexes have been proposed, [18][19][20][21][22][23][24][25][26] and a book about modeling these interactions is about to appear (Modeling Peptide-Protein Interactions, to appear in the Methods in Molecular Biology Series, Ed. Springer).…”

Section: Introductionmentioning

confidence: 99%

FlexPepDock lessons from CAPRI peptide–protein rounds and suggested new criteria for assessment of model quality and utility

et al. 2017

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“…Due to the high flexibility, peptide docking involves more exhaustive calculations. To circumvent this impediments several attempt have been made: 1) the use of templates extracted from monomeric ensembles and filtering by energy restraints (Alogheli, Olanders, Schaal, Brandt & Karlén, 2017;Dominguez et al, 2003;Yan, Xu & Zou, 2016), 2) applying restraints and optimizing orientation using local search (Sacquin-Mora & Prévost, 2015), 3) simulatedannealing molecular dynamics and anchor-spot detection on the binding site (Ben-Shimon & Niv, 2015) and 4) systematic selection of rotatable bonds based on latin squares approach to identify energy minima (Paul & Gautham, 2017).…”

Section: Docking Peptides or Peptide-like Ligandsmentioning

confidence: 99%

Acoplamiento Molecular: Avances Recientes y Retos

2018

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Nota: Artículo recibido el 18 de octubre de 2017 y aceptado el 02 de mayo de 2018. ARTÍCULO DE REVISIÓN abstractAutomated molecular docking aims at predicting the possible interactions between two molecules. This method has proven useful in medicinal chemistry and drug discovery providing atomistic insights into molecular recognition. Over the last 20 years methods for molecular docking have been improved, yielding accurate results on pose prediction. Nonetheless, several aspects of molecular docking need revision due to changes in the paradigm of drug discovery. In the present article, we review the principles, techniques, and algorithms for docking with emphasis on protein-ligand docking for drug discovery. We also discuss current approaches to address major challenges of docking.Key Words: chemoinformatics, computer-aided drug design, drug discovery, structure-activity relationships. Acoplamiento Molecular: Avances Recientes y Retos resuMenEl acoplamiento molecular automatizado tiene como objetivo proponer un modelo de unión entre dos moléculas. Este método ha sido útil en química farmacéutica y en el descubrimiento de nuevos fármacos por medio del entendimiento de las fuerzas de interacción involucradas en el reconocimiento molecular. Durante los últimos 20 años se ha modificado extensamente la técnica de acoplamiento molecular dando resultados precisos en la predicción de los modos de unión. Sin embargo, hay algunas áreas que requieren ser mejoradas substancialmente. En este trabajo se revisan principios, técnicas y algoritmos usados en los programas computacionales del acoplamiento molecular con enfoque en la interacción proteína-ligando aplicado al descubrimiento de nuevos fármacos. También se discuten las estrategias dirigidas a solucionar los principales retos de esta técnica computacional.Palabras Clave: descubrimiento de nuevos fármacos, diseño de fármacos asistido por computadora, quimioinformática, relaciones estructura-actividad.

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“…Peptide-protein docking is typically a two-step process in which a coarse-grained exploration of the possible binding modes is followed by high-resolution refinement of those having lower energies [10][11][12][13][14][15][16][17][18][19]. During the exploratory step, the peptide may be forced to adopt a limited number of pregenerated conformations, which are docked as rigid bodies at the binding site (local docking) or, when that is unknown, around the entire surface of the receptor (global docking).…”

Section: Introductionmentioning

confidence: 99%

Receptor-free Discrimination of Peptide Native Bound Conformations Suggests Limited Transferability of the “cen_std+score4l” Rosetta Energy Function From Proteins to Peptides

Bazzoli¹,

Contini²

2020

Preprint

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One of the strategies of peptide–protein docking is to pregenerate an ensemble of peptide conformations in the absence of the receptor, and then dock them as rigid bodies onto its surface. Success of this strategy requires that the scoring function that drives the pregeneration step be able to discriminate in favor of conformations that resemble the native bound conformation. Here we present a study on the discrimination of peptide native bound conformations as achieved without receptor by the “cen_std+score4L” Rosetta energy function, a low-resolution scoring function equivalent to one chosen for other tasks where the modeling of solvent effects is of special importance. The cen_std+score4L function was able to assign, on average, lower energies to native-like than to non-native decoy conformations for only 3 of our 18 test peptides; it also ranked one or more native-like decoys in the top 1% for only 2 peptides. However, by optimizing the weights of the energy terms that define the cen_std+score4L function, native discrimination improved substantially: Native-like decoys were assigned lower energies than non-native decoys for 16 peptides, with a discrimination signal larger than noise for 9 peptides, that is, 50% of the test set. And for 9 peptides, too, native-like decoys ranked in the top 1%. An ensuing energetic analysis of native-like versus non-native decoys suggests that native peptide conformations have solvation and non-local electrostatics that poorly recapitulate those of native protein conformations. Native peptide conformations are also characterized by few backbone–backbone H-bonds and by lack of compactness, presumably to optimize interaction with the receptor. Overall, this study lays groundwork for pregenerating dockable peptide conformations with Rosetta, whether the subsequent docking will be performed by Rosetta or some other software.

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Fully Blind Docking at the Atomic Level for Protein-Peptide Complex Structure Prediction

Cited by 87 publications

References 49 publications

FlexPepDock lessons from CAPRI peptide–protein rounds and suggested new criteria for assessment of model quality and utility

FlexPepDock lessons from CAPRI peptide–protein rounds and suggested new criteria for assessment of model quality and utility

Acoplamiento Molecular: Avances Recientes y Retos

Receptor-free Discrimination of Peptide Native Bound Conformations Suggests Limited Transferability of the “cen_std+score4l” Rosetta Energy Function From Proteins to Peptides

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