scholarly journals FlexAID: Revisiting docking on non native-complex structures

2014 ◽  
Author(s):  
Francis Gaudreault ◽  
Rafael Najmanovich
Keyword(s):  
Virtual Screening ◽  
Small Molecule ◽  
A Priori ◽  
Scoring Function ◽  
Protein Docking ◽  
Side Chain ◽  
Complex Structures ◽  
Autodock Vina ◽  
Chain Flexibility ◽  
Better Than

Small-molecule protein docking is an essential tool in drug design and to understand molecular recognition. In the present work we introduce FlexAID, a small-molecule docking algorithm that accounts for target side-chain flexibility and utilizes a soft scoring function, i.e. one that is not highly dependent on specific geometric criteria, based on surface complementarity. The pairwise energy parameters were derived from a large dataset of true positive poses and negative decoys from the PDBbind dataset through an iterative process using Monte Carlo simulations. The prediction of binding poses is tested using the independent Astex dataset while performance in virtual screening is evaluated using a subset of the DUD dataset. We compare FlexAID to AutoDock Vina, FlexX, and rDock in an extensive number of scenarios to understand the strengths and limitations of the different programs as well as to reported results for Glide, GOLD and DOCK6 where applicable. The most relevant among these scenarios is that of docking on flexible non native-complex structures where as is the case in reality, the target conformation in the bound form is not known a priori. We demonstrate that FlexAID, unlike other programs, is robust against increasing structural variability. FlexAID obtains equivalent sampling success as GOLD and performs better than AutoDock Vina or FlexX in all scenarios against non native- complex structures. FlexAID is better than rDock when there is at least one critical side-chain movement required upon ligand binding. In virtual screening, FlexAID rescored results are comparable to those of AutoDock Vina and rDock. The higher accuracy in flexible targets where critical movements are required, intuitive PyMOL-integrated graphical user interface and free source code as well as pre-compiled executables for Windows, Linux and Mac OS make FlexAID a welcome addition to the arsenal of existing small-molecule protein docking methods.

scholarly journals GNINA 1.0: molecular docking with deep learning

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Andrew T. McNutt ◽  
Paul Francoeur ◽  
Rishal Aggarwal ◽  
Tomohide Masuda ◽  
Rocco Meli ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Root Mean Square ◽  
Root Mean Square Deviation ◽  
Computational Cost ◽  
Scoring Function ◽  
Binding Pocket ◽  
Protein Docking ◽  
Mean Square ◽  
Mean Square Deviation ◽  
Autodock Vina

AbstractMolecular docking computationally predicts the conformation of a small molecule when binding to a receptor. Scoring functions are a vital piece of any molecular docking pipeline as they determine the fitness of sampled poses. Here we describe and evaluate the 1.0 release of the Gnina docking software, which utilizes an ensemble of convolutional neural networks (CNNs) as a scoring function. We also explore an array of parameter values for Gnina 1.0 to optimize docking performance and computational cost. Docking performance, as evaluated by the percentage of targets where the top pose is better than 2Å root mean square deviation (Top1), is compared to AutoDock Vina scoring when utilizing explicitly defined binding pockets or whole protein docking. Gnina, utilizing a CNN scoring function to rescore the output poses, outperforms AutoDock Vina scoring on redocking and cross-docking tasks when the binding pocket is defined (Top1 increases from 58% to 73% and from 27% to 37%, respectively) and when the whole protein defines the binding pocket (Top1 increases from 31% to 38% and from 12% to 16%, respectively). The derived ensemble of CNNs generalizes to unseen proteins and ligands and produces scores that correlate well with the root mean square deviation to the known binding pose. We provide the 1.0 version of Gnina under an open source license for use as a molecular docking tool at https://github.com/gnina/gnina.

scholarly journals GNINA 1.0: Molecular Docking with Deep Learning

2021 ◽  
Author(s):  
Andrew McNutt ◽  
Paul Francoeur ◽  
Rishal Aggarwal ◽  
Tomohide Masuda ◽  
Rocco Meli ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Root Mean Square ◽  
Root Mean Square Deviation ◽  
Computational Cost ◽  
Scoring Function ◽  
Binding Pocket ◽  
Protein Docking ◽  
Mean Square ◽  
Mean Square Deviation ◽  
Autodock Vina

Molecular docking computationally predicts the conformation of a small molecule when binding to a receptor. Scoring functions are a vital piece of any molecular docking pipeline as they determine the fitness of sampled poses. Here we describe and evaluate the 1.0 release of the Gnina docking software, which utilizes an ensemble of convolutional neural networks (CNNs) as a scoring function. We also explore an array of parameter values for Gnina 1.0 to optimize docking performance and computational cost. Docking performance, as evaluated by the percentage of targets where the top pose is better than 2A root mean square deviation (Top1), is compared to AutoDock Vina scoring when utilizing explicitly defined binding pockets or whole protein docking. Gnina, utilizing a CNN scoring function to rescore the output poses, outperforms AutoDock Vina scoring on redocking and cross-docking tasks when the binding pocket is defined (Top1 increases from 58% to 73% and from 27% to 37%, respectively) and when the whole protein defines the binding pocket (Top1 increases from 31% to 38% and from 12% to 16%, respectively). The derived ensemble of CNNs generalizes to unseen proteins and ligands and produces scores that correlate well with the root mean square deviation to the known binding pose. We provide the 1.0 version of Gnina under and open source license for use as a molecular docking tool at https://github.com/gnina/gnina.

scholarly journals Virtual Screening with Gnina 1.0

2021 ◽  
Author(s):  
Jocelyn Sunseri ◽  
David Koes
Keyword(s):  
Virtual Screening ◽  
Scoring Function ◽  
Compound Library ◽  
Autodock Vina ◽  
Convolutional Networks ◽  
Development Costs ◽  
Screening Performance ◽  
Computationally Intensive ◽  
Speed And Accuracy ◽  
Virtual Screening Performance

Virtual screening - predicting which compounds within a specified compound library bind to a target molecule, typically a protein - is a fundamental task in the field of drug discovery. Doing virtual screening well provides tangible practical benefits, including reduced drug development costs, faster time to therapeutic viability, and fewer unforeseen side effects. As with most applied computational tasks, the algorithms currently used to perform virtual screening feature inherent tradeoffs between speed and accuracy. Furthermore, even theoretically rigorous, computationally intensive methods may fail to account for important effects relevant to whether a given compound will ultimately be usable as a drug. Here we investigate the virtual screening performance of the recently released Gnina molecular docking software, which uses deep convolutional networks to score protein-ligand structures. We find, on average, that Gnina outperforms conventional empirical scoring. The default scoring in Gnina outperforms the empirical AutoDock Vina scoring function on 89 of the 117 targets of the DUD-E and LIT-PCBA virtual screening benchmarks with a median 1% early enrichment factor that is more than twice that of Vina. However, we also find that issues of bias linger in these sets, even when not used directly to train models, and this bias obfuscates to what extent machine learning models are achieving their performance through a sophisticated interpretation of molecular interactions versus fitting to non-informative simplistic property distributions.

scholarly journals GNINA 1.0: Molecular Docking with Deep Learning

2021 ◽  
Author(s):  
Andrew McNutt ◽  
Paul Francoeur ◽  
Rishal Aggarwal ◽  
Tomohide Masuda ◽  
Rocco Meli ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Root Mean Square ◽  
Root Mean Square Deviation ◽  
Computational Cost ◽  
Scoring Function ◽  
Binding Pocket ◽  
Protein Docking ◽  
Mean Square ◽  
Mean Square Deviation ◽  
Autodock Vina

Molecular docking computationally predicts the conformation of a small molecule when binding to a receptor. Scoring functions are a vital piece of any molecular docking pipeline as they determine the fitness of sampled poses. Here we describe and evaluate the 1.0 release of the Gnina docking software, which utilizes an ensemble of convolutional neural networks (CNNs) as a scoring function. We also explore an array of parameter values for Gnina 1.0 to optimize docking performance and computational cost. Docking performance, as evaluated by the percentage of targets where the top pose is better than 2A root mean square deviation (Top1), is compared to AutoDock Vina scoring when utilizing explicitly defined binding pockets or whole protein docking. Gnina, utilizing a CNN scoring function to rescore the output poses, outperforms AutoDock Vina scoring on redocking and cross-docking tasks when the binding pocket is defined (Top1 increases from 58% to 73% and from 27% to 37%, respectively) and when the whole protein defines the binding pocket (Top1 increases from 31% to 38% and from 12% to 16%, respectively). The derived ensemble of CNNs generalizes to unseen proteins and ligands and produces scores that correlate well with the root mean square deviation to the known binding pose. We provide the 1.0 version of Gnina under and open source license for use as a molecular docking tool at https://github.com/gnina/gnina.

scholarly journals Discovery of Novel DUSP16 Phosphatase Inhibitors through Virtual Screening with Homology Modeled Protein Structure

2014 ◽  
Vol 19 (10) ◽  
pp. 1383-1390 ◽  
Author(s):  
Hwangseo Park ◽  
So Ya Park ◽  
Sang-Won Nam ◽  
Seong Eon Ryu
Keyword(s):  
Virtual Screening ◽  
Scoring Function ◽  
Structural Features ◽  
Protein Docking ◽  
Target Protein ◽  
Phosphatase Inhibitors ◽  
Docking Simulations ◽  
Drug Candidates ◽  
Promising Therapeutic Target

Recently, dual-specificity phosphatase 16 (DUSP16) emerged as a promising therapeutic target protein for the development of anti-atherosclerosis and anticancer medicines. The present study was undertaken to identify the novel inhibitors of DUSP16 based on the structure-based virtual screening. We have been able to find seven novel inhibitors of DUSP16 through the drug design protocol involving homology modeling of the target protein, docking simulations between DUSP16 and its putative inhibitors with the modified scoring function, and in vitro enzyme assay. These inhibitors revealed good potency, with IC50 values ranging from 1 to 22 µM, and they were also screened computationally for having desirable physicochemical properties as drug candidates. Therefore, they deserve consideration for further development by structure-activity relationship studies to optimize the inhibitory activity against DUSP16. Structural features relevant to the stabilization of the newly identified inhibitors in the active site of DUSP16 are addressed in detail.

Sign in / Sign up

Export Citation Format

Share Document