Docking Flexible Cyclic Peptides with <i>AutoDock CrankPep</i>

Zhang, Yuqi; Sanner, Michel F.

doi:10.1021/acs.jctc.9b00557

Cited by 34 publications

(44 citation statements)

References 44 publications

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“…AutoDock Vina (Vina) 1 is one of the docking engines in the AutoDock Suite 2 , together with AutoDock4 (AD4) 3 , AutoDockGPU 4 , AutoDockFR 5 , and AutoDock-CrankPep 6 . Vina is arguably among the most widely used docking engines, probably because of its ease of use and speed, when compared to the other docking engines in the suite and elsewhere, as well as being open source.…”

Section: Introductionmentioning

confidence: 99%

AutoDock Vina 1.2.0: New Docking Methods, Expanded Force Field, and Python Bindings

Eberhardt¹,

Santos-Martins²,

Tillack³

et al. 2021

Preprint

114

143

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<pre>AutoDock Vina is arguably one of the fastest and most widely used open-source docking engines. However, compared to other docking engines in the AutoDock Suite, it lacks features that support modeling of specific systems such as macrocycles or modeling water explicitly. Here, we describe the implementation of these functionality in AutoDock Vina 1.2.0. Additionally, AutoDock Vina 1.2.0 supports the AutoDock4.2 scoring function, simultaneous docking of multiple ligands, and a batch mode for docking a large number of ligands. Furthermore, we implemented Python bindings to facilitate scripting and the development of docking workflows. This work is an effort toward the unification of the features of the AutoDock4 and AutoDock Vina docking engines. The source code is available at <a href="https://github.com/ccsb-scripps/AutoDock-Vina" rel="noopener noreferrer" target="_blank">https://github.com/ccsb-scripps/AutoDock-Vina</a></pre>

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Section: Introductionmentioning

confidence: 99%

AutoDock Vina 1.2.0: New Docking Methods, Expanded Force Field, and Python Bindings

Eberhardt¹,

Santos-Martins²,

Tillack³

et al. 2021

Preprint

114

143

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“…The two datasets used to benchmark the protocols in this study are composed of 30 cyclic peptide-protein complexes extracted from the dataset described by Zhang et al 26 . The first set of complexes, the Backbone dataset, includes 18 complexes in which the peptide is cyclized through its N- and C-termini with a minimum sequence length of six amino acids (Table S1).…”

Section: Methodsmentioning

confidence: 99%

A Cyclisation and Docking Protocol for Cyclic Peptide-Protein Modelling using HADDOCK2.4

Charitou

Keulen

Bonvin

2022

Preprint

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An emerging class of therapeutic molecules are cyclic peptides with over 40 cyclic peptide drugs currently in clinical use. Their mode of action is, however, not fully understood, impeding rational drug design. Computational techniques could positively impact their design but modeling them and their interactions remains challenging due to their cyclic nature and their flexibility. This study presents a step-by-step protocol for generating cyclic peptide conformations and docking them to their protein target using HADDOCK2.4. A dataset of 30 cyclic peptide-protein complexes was used to optimize both cyclisation and docking protocols. It supports peptides cyclized via an N- and C- terminus peptide bond and/or a disulfide bond. An ensemble of cyclic peptide conformations is then used in HADDOCK to dock them onto their target protein using knowledge of the binding site on the protein side to drive the modeling. The presented protocol predicts at least one acceptable model according to CAPRI criteria (Critical Assessment of Prediction of Interaction) for each complex of the dataset when the top 10 HADDOCK-ranked single structures are considered (100% success rate top10) both in the bound and unbound docking scenario. Moreover, its performance in both bound and fully unbound docking, is similar to the state-of-the-art software in the field, Autodock CrankPep. The presented cyclisation and docking protocol should make HADDOCK a valuable tool for rational cyclic peptide-based drug design and high-throughput screening.

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“…ADCP, which currently allows consistent docking of peptides with up to 20 amino acids 25 (Figure 7), is at the cutting edge of this field in a recent evaluation of 14 peptide docking programs 61 . ADCP can also cyclize peptides head‐to‐tail (Figure 7a) and/or by forming up to two disulfide bridges when cysteines are present (Figure 7b) thus supporting molecules with multiple cycles 27 . Cyclization is achieved on‐the‐fly during the docking simulation by using potentials to pull the N‐ and C‐termini or cysteine sulfur atoms together while ignoring steric repulsion between these atoms.…”

Section: Peptide Dockingmentioning

confidence: 99%

“…61 ADCP can also cyclize peptides head-to-tail ( Figure 7a) and/or by forming up to two disulfide bridges when cysteines are present (Figure 7b) thus supporting molecules with multiple cycles. 27 Cyclization is achieved on-the-fly during the docking simulation by using potentials to pull the N-and C-termini or cysteine sulfur atoms together while ignoring steric repulsion between these atoms. In the latter case, the pairing of the cysteines does not need to be specified by the user, but instead results from the docking.…”

Section: Peptide Dockingmentioning

confidence: 99%

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The AutoDock suite at 30

et al. 2020

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The AutoDock suite provides a comprehensive toolset for computational ligand docking and drug design and development. The suite builds on 30 years of methods development, including empirical free energy force fields, docking engines, methods for site prediction, and interactive tools for visualization and analysis. Specialized tools are available for challenging systems, including covalent inhibitors, peptides, compounds with macrocycles, systems where ordered hydration plays a key role, and systems with substantial receptor flexibility. All methods in the AutoDock suite are freely available for use and reuse, which has engendered the continued growth of a diverse community of primary users and third‐party developers.

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Docking Flexible Cyclic Peptides with AutoDock CrankPep

Cited by 34 publications

References 44 publications

AutoDock Vina 1.2.0: New Docking Methods, Expanded Force Field, and Python Bindings

AutoDock Vina 1.2.0: New Docking Methods, Expanded Force Field, and Python Bindings

A Cyclisation and Docking Protocol for Cyclic Peptide-Protein Modelling using HADDOCK2.4

The AutoDock suite at 30

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