Protein–ligand pose and affinity prediction: Lessons from D3R Grand Challenge 3

Koukos, Panagiotis I.; Li, Xue; Bonvin, Alexandre M. J. J.

doi:10.1007/s10822-018-0148-4

Cited by 27 publications

(36 citation statements)

References 27 publications

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“…This Table reports the statistics of the RMSD calculated on heavy atoms of each ligand after structural alignment on the reference conformation extracted from the experimental structure of the corresponding complex (hereafter indicated as RMSDlig-fit, to be distinguished from the same value calculated in the complex after alignment of the protein interface region). In all cases the minimum RMSDlig-fit values are lower than 2 Å, confirming the accuracy of the approach in reproducing near-native conformations [16]. Overall, these values are slightly larger than those obtained for the sampling of holo-like conformations of the receptor (2nd and 3rd columns in Table 3).…”

Section: Generation Of Near-native Ligand Conformerssupporting

confidence: 52%

“…In order to identify the putative binding site of the protein, we used the same approach presented in [16]. Namely, we retrieved in the PDB all the structures featuring at least 95% sequence identity to the amino acid sequence provided by the organizers and having a co-crystallized ligand (other than crystallization buffer molecules); this resulted in 340 entries.…”

Section: Binding Site Determinationmentioning

confidence: 99%

“…The method for generating ligand structures [16] selects a number (here 10) of ligand conformations out of a pool of (500) conformers generated using the OpenEye Omega software [19] by searching for similar structures on the PDB based on the matching between extended Tanimoto coefficients. As such it still does require available 3D structures of related ligands bound to the receptor to be present in the PDB.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Coupling enhanced sampling of the apo-receptor with template-based ligand conformers selection: performance in pose prediction in the D3R Grand Challenge 4

Basciu¹,

Koukos

Malloci³

et al. 2019

J Comput Aided Mol Des

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We report the performance of our newly introduced Ensemble Docking with Enhanced sampling of pocket Shape (EDES) protocol coupled to a template-based algorithm to generate near-native ligand conformations in the 2019 iteration of the Grand Challenge organized by the D3R consortium. Using either AutoDock4.2 or HADDOCK2.2 docking programs (each software in two variants of the protocol) our method generated native-like poses among the top 5 submitted for evaluation for most of the 20 targets with similar performances. The protein selected for GC4 was the human beta-site amyloid precursor protein cleaving enzyme 1 (BACE-1), a transmembrane aspartic-acid protease. We identified at least one pose whose heavy-atoms RMSD was less than 2.5 Å from the native conformation for 16 (80%) and 17 (85%) of the twenty targets using AutoDock and HADDOCK, respectively. Dissecting the possible sources of errors revealed that: i) our EDES protocol (with minor modifications) was able to sample sub-ångstrom conformations for all 20 protein targets, reproducing the correct conformation of the binding site within ~1 Å RMSD; ii) as already shown by some of us in GC3, even in the presence of near-native protein structures, a proper selection of ligand conformers is crucial for the success of ensemble-docking calculations. Importantly, our approach performed best among the protocols exploiting only structural information of the apo protein to generate conformations of the receptor for ensemble-docking calculations.

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Section: Generation Of Near-native Ligand Conformerssupporting

confidence: 52%

Section: Binding Site Determinationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Coupling enhanced sampling of the apo-receptor with template-based ligand conformers selection: performance in pose prediction in the D3R Grand Challenge 4

Basciu¹,

Koukos

Malloci³

et al. 2019

J Comput Aided Mol Des

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“…1,12,29,32−34 In particular, the inclusion of experimental structures of proteins bound to ligands similar to the one of interest has been shown to significantly increase the accuracy of docking. 5,8,12,32,35 Unfortunately, the number of targets whose three-dimensional (3D) structures have been experimentally solved remains limited compared with the druggable genome. 4,36 Furthermore, the exploration of different con-formations in experimental structures is generally limited and biased toward (often just a few) known ligand−receptor complexes, which impacts the chemical diversity of putative lead compounds in virtual screening campaigns.…”

Section: ■ Introductionmentioning

confidence: 99%

Holo-like and Druggable Protein Conformations from Enhanced Sampling of Binding Pocket Volume and Shape

Basciu

Malloci

Pietrucci

et al. 2019

J. Chem. Inf. Model.

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Understanding molecular recognition of small molecules by proteins in atomistic detail is key for drug design. Molecular docking is a widely used computational method to mimic ligand−protein association in silico. However, predicting conformational changes occurring in proteins upon ligand binding is still a major challenge. Ensemble docking approaches address this issue by considering a set of different conformations of the protein obtained either experimentally or from computer simulations, e.g., molecular dynamics. However, holo structures prone to host (the correct) ligands are generally poorly sampled by standard molecular dynamics simulations of the apo protein. In order to address this limitation, we introduce a computational approach based on metadynamics simulations called ensemble docking with enhanced sampling of pocket shape (EDES) that allows holo-like conformations of proteins to be generated by exploiting only their apo structures. This is achieved by defining a set of collective variables that effectively sample different shapes of the binding site, ultimately mimicking the steric effect due to the ligand. We assessed the method on three challenging proteins undergoing different extents of conformational changes upon ligand binding. In all cases our protocol generates a significant fraction of structures featuring a low RMSD from the experimental holo geometry. Moreover, ensemble docking calculations using those conformations yielded in all cases native-like poses among the top-ranked ones.

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“…The success of ensemble-docking is strongly dependent on the ability to include, in the pool of receptor structures, some conformation similar to the one found in the true complex 1,12,27,[31][32][33] . In particular, it has been shown that the inclusion of experimental structures of proteins bound to ligands similar to the one of interest significantly increased the accuracy of the method 5,8,12,31,34 . However, with reference to the druggable genome 35 , the number of targets whose three-dimensional structure has been experimentally solved remains still limited 4 .…”

Section: Introductionmentioning

confidence: 99%

An enhanced-sampling MD-based protocol for molecular docking

Basciu

Malloci

Pietrucci

et al. 2018

Preprint

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Understanding molecular recognition of proteins by small molecules is key for drug design. Despite the number of experimental structures of ligand-protein complexes keeps growing, the number of available targets remains limited compared to the druggable genome, and structural diversity is generally low, which affects the chemical variance of putative lead compounds. From a computational perspective, molecular docking is widely used to mimic ligand-protein association in silico. Ensembledocking approaches include flexibility through a set of different conformations of the protein obtained either experimentally or from computer simulations, e.g. molecular dynamics. However, structures prone to host (the correct) ligands are generally poorly sampled by standard molecular dynamics simulations of the apo protein. In order to address this limitation, we introduce a computational approach based on metadynamics simulations (EDES -Ensemble-Docking with Enhanced-sampling of pocket Shape) to generate druggable conformations of proteins only exploiting their apo structures. This is achieved by defining a set of collective variables that effectively sample different shapes of the binding site, ultimately mimicking the steric effect due to ligands to generate holo-like binding site geometries. We assessed the method on two challenging proteins undergoing different extents of conformational changes upon ligand binding. In both cases our protocol generated a significant fraction of structures featuring a low RMSD from the experimental holo conformation. Moreover, ensemble docking calculations using those conformations yielded native-like poses among the top ranked ones for both targets. This proof of concept study paves the route towards an automated workflow to generate druggable conformations of proteins, which should become a precious tool for structure-based drug design.

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Protein–ligand pose and affinity prediction: Lessons from D3R Grand Challenge 3

Cited by 27 publications

References 27 publications

Coupling enhanced sampling of the apo-receptor with template-based ligand conformers selection: performance in pose prediction in the D3R Grand Challenge 4

Coupling enhanced sampling of the apo-receptor with template-based ligand conformers selection: performance in pose prediction in the D3R Grand Challenge 4

Holo-like and Druggable Protein Conformations from Enhanced Sampling of Binding Pocket Volume and Shape

An enhanced-sampling MD-based protocol for molecular docking

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