How Good Are State-of-the-Art Docking Tools in Predicting Ligand Binding Modes in Protein–Protein Interfaces?

Krüger, Dennis M.; Jessen, Gisela; Gohlke, Holger

doi:10.1021/ci3003599

Cited by 29 publications

(44 citation statements)

References 26 publications

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“…For this, distance-dependent pair-potentials were derived from 851 crystallographically determined protein-protein complexes. DrugScore PPI is based on the DrugScore approach that has proven successful already as a scoring and objective function for protein-ligand [38], [45], [46], [47] and RNA-ligand [39], [48] complexes. In part, this has been attributed to the implicit, well-balanced consideration of several different types of interactions important for molecular recognition, such as polar (including hydrogen bonding), charged, and nonpolar interactions.…”

Section: Introductionmentioning

confidence: 99%

DrugScorePPI Knowledge-Based Potentials Used as Scoring and Objective Function in Protein-Protein Docking

et al. 2014

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The distance-dependent knowledge-based DrugScorePPI potentials, previously developed for in silico alanine scanning and hot spot prediction on given structures of protein-protein complexes, are evaluated as a scoring and objective function for the structure prediction of protein-protein complexes. When applied for ranking “unbound perturbation” (“unbound docking”) decoys generated by Baker and coworkers a 4-fold (1.5-fold) enrichment of acceptable docking solutions in the top ranks compared to a random selection is found. When applied as an objective function in FRODOCK for bound protein-protein docking on 97 complexes of the ZDOCK benchmark 3.0, DrugScorePPI/FRODOCK finds up to 10% (15%) more high accuracy solutions in the top 1 (top 10) predictions than the original FRODOCK implementation. When used as an objective function for global unbound protein-protein docking, fair docking success rates are obtained, which improve by ∼2-fold to 18% (58%) for an at least acceptable solution in the top 10 (top 100) predictions when performing knowledge-driven unbound docking. This suggests that DrugScorePPI balances well several different types of interactions important for protein-protein recognition. The results are discussed in view of the influence of crystal packing and the type of protein-protein complex docked. Finally, a simple criterion is provided with which to estimate a priori if unbound docking with DrugScorePPI/FRODOCK will be successful.

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Section: Introductionmentioning

confidence: 99%

DrugScorePPI Knowledge-Based Potentials Used as Scoring and Objective Function in Protein-Protein Docking

et al. 2014

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“…Numerous docking methodologies have been developed in recent years and many useful comparisons of the re‐docking ability of these software packages have been published . Recently, the Gohlke research group published a comparative study of current docking programs for re‐docking of ligand‐receptor pairs in the CCDC/Astex dataset .…”

Section: Resultsmentioning

confidence: 99%

“…Each docking trial for the MD + minimization and the simulated annealing protocol was limited to 2.6 × 10 6 energy evaluations to allow the fairest comparison with the benchmark studies by Gohlke et al The docking trials were repeated 10 times, with the same input structures for the protein and ligand. However, each trial generated a new set of random orientations and conformations of the small molecule followed by sampling on the grid representation of the receptor.…”

Section: Methodsmentioning

confidence: 99%

Flexible CDOCKER: Development and application of a pseudo‐explicit structure‐based docking method within CHARMM

2015

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Protein-ligand docking is a commonly used method for lead identification and refinement. While traditional structure-based docking methods represent the receptor as a rigid body, recent developments have been moving toward the inclusion of protein flexibility. Proteins exist in an inter-converting ensemble of conformational states, but effectively and efficiently searching the conformational space available to both the receptor and ligand remains a well-appreciated computational challenge. To this end, we have developed the Flexible CDOCKER method as an extension of the family of complete docking solutions available within CHARMM. This method integrates atomically detailed side chain flexibility with grid-based docking methods, maintaining efficiency while allowing the protein and ligand configurations to explore their conformational space simultaneously. This is in contrast to existing approaches that use induced-fit like sampling, such as Glide or Autodock, where the protein or the ligand space is sampled independently in an iterative fashion. Presented here are developments to the CHARMM docking methodology to incorporate receptor flexibility and improvements to the sampling protocol as demonstrated with re-docking trials on a subset of the CCDC/Astex set. These developments within CDOCKER achieve docking accuracy competitive with or exceeding the performance of other widely utilized docking programs.

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“…Although there are some examples where CADD has been applied to SMPPII discovery [21–23], PPIs remain difficult targets since PPIs and their binding ligands have different characteristics from traditional targets. [24, 25]. To understand the current status of CADD methods for PPI targets, here we review computational tools for discovering SMPPIIs, which try to overcome the hurdles that arise from the different nature of PPIs and compounds that interact with PPIs.…”

Section: Introductionmentioning

confidence: 99%

In silico structure-based approaches to discover protein-protein interaction-targeting drugs

Shin

Christoffer

Kihara

2017

Methods

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A core concept behind modern drug discovery is finding a small molecule that modulates a function of a target protein. This concept has been successfully applied since the mid-1970s. However, the efficiency of drug discovery is decreasing because the druggable target space in the human proteome is limited. Recently, protein-protein interaction (PPI) has been identified as an emerging target space for drug discovery. PPI plays a pivotal role in biological pathways including diseases. Current human interactome research suggests that the number of PPIs ranges from 130,000 to 650,000, and only a small number of them have been targeted as drug targets. For traditional drug targets, in silico structure-based methods have been successful in many cases. However, their performance suffers on PPI interfaces because PPI interfaces are different in five major aspects: From a geometric standpoint, they have relatively large interface regions, flat geometry, and the interface surface shape tends to fluctuate upon binding. Also, their interactions are dominated by hydrophobic atoms, which is different from traditional binding-pocket-targeted drugs. Finally, PPI targets usually lack natural molecules that bind to the target PPI interface. Here, we first summarize characteristics of PPI interfaces and their known binders. Then, we will review existing in silico structure-based approaches for discovering small molecules that bind to PPI interfaces.

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How Good Are State-of-the-Art Docking Tools in Predicting Ligand Binding Modes in Protein–Protein Interfaces?

Cited by 29 publications

References 26 publications

DrugScorePPI Knowledge-Based Potentials Used as Scoring and Objective Function in Protein-Protein Docking

DrugScorePPI Knowledge-Based Potentials Used as Scoring and Objective Function in Protein-Protein Docking

Flexible CDOCKER: Development and application of a pseudo‐explicit structure‐based docking method within CHARMM

In silico structure-based approaches to discover protein-protein interaction-targeting drugs

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