scholarly journals Incorporating Backbone Flexibility in MedusaDock Improves Ligand-Binding Pose Prediction in the CSAR2011 Docking Benchmark

2012 ◽  
Vol 53 (8) ◽  
pp. 1871-1879 ◽  
Author(s):  
Feng Ding ◽  
Nikolay V. Dokholyan
Keyword(s):  
Ligand Binding ◽  
Pose Prediction ◽  
Backbone Flexibility ◽  
Docking Benchmark

scholarly journals Middle-way flexible docking: Pose prediction using mixed-resolution Monte Carlo in estrogen receptor α

2018 ◽  
Author(s):  
Justin Spiriti ◽  
Sundar Raman Subramanian ◽  
Rohith Palli ◽  
Maria Wu ◽  
Daniel M. Zuckerman
Keyword(s):  
Monte Carlo ◽  
Estrogen Receptor ◽  
Computational Cost ◽  
Protein Flexibility ◽  
Estrogen Receptor Α ◽  
Pose Prediction ◽  
Backbone Flexibility ◽  
Flexible Docking ◽  
Mixed Resolution

AbstractThere is a vast gulf between the two primary strategies for simulating protein-ligand interactions. Docking methods significantly limit or eliminate protein flexibility to gain great speed at the price of uncontrolled inaccuracy, whereas fully flexible atomistic molecular dynamics simulations are expensive and often suffer from limited sampling. We have developed a flexible docking approach geared especially for highly flexible or poorly resolved targets based on mixed-resolution Monte Carlo (MRMC), which is intended to offer a balance among speed, protein flexibility, and sampling power. The binding region of the protein is treated with a standard atomistic force field, while the remainder of the protein is modeled at the residue level with a Gō model that permits protein flexibility while saving computational cost. Implicit solvation is used. Here we assess three facets of the MRMC approach with implications for other docking studies: (i) the role of receptor flexibility in cross-docking pose prediction; (ii) the use of non-equilibrium candidate Monte Carlo (NCMC) and (iii) the use of pose-clustering in scoring. We examine 61 co-crystallized ligands of estrogen receptor α, an important cancer target known for its flexibility. We also compare the performance of the MRMC approach with Autodock smina, a docking program. [1] Adding protein flexibility, not surprisingly, leads to significantly lower total energies and stronger interactions between protein and ligand, but notably we document the important role of backbone flexibility in the improvement. The improved backbone flexibility also leads to improved performance relative to smina. Somewhat unexpectedly, our implementation of NCMC leads to only modestly improved sampling of ligand poses. Overall, the addition of protein flexibility improves the performance of docking, as measured by energy-ranked poses, but we do not find significant improvements based on cluster information or the use of NCMC.

Higher Accuracy Achieved for Protein–Ligand Binding Pose Prediction by Elastic Network Model-Based Ensemble Docking

2020 ◽  
Vol 60 (6) ◽  
pp. 2939-2950 ◽  
Author(s):  
Anhui Wang ◽  
Yuebin Zhang ◽  
Huiying Chu ◽  
Chenyi Liao ◽  
Zhichao Zhang ◽  
...  
Keyword(s):  
Ligand Binding ◽  
Network Model ◽  
Pose Prediction ◽  
Elastic Network Model ◽  
Elastic Network ◽  
Ensemble Docking ◽  
Model Based

Fragment Pose Prediction Using Non-Equilibrium Candidate Monte Carlo and Molecular Dynamics Simulations

2019 ◽  
Author(s):  
Nathan M. Lim ◽  
Meghan Osato ◽  
Gregory L. Warren ◽  
David L. Mobley
Keyword(s):  
Crystal Structure ◽  
Molecular Dynamics ◽  
Monte Carlo ◽  
Ligand Binding ◽  
Md Simulations ◽  
Binding Mode ◽  
Protein Crystal ◽  
Pose Prediction ◽  
Binding Modes ◽  
Non Equilibrium

<div>Part of early stage drug discovery involves determining how molecules may bind to the target protein. Through understanding where and how molecules bind, chemists can begin to build ideas on how to design improvements to increase binding affinities. In this retrospective study, we compare how computational approaches like docking, molecular dynamics (MD) simulations, and a non-equilibrium candidate Monte Carlo (NCMC) based method (NCMC+MD) perform in predicting binding modes for a set of 12 fragment-like molecules which bind to soluble epoxide hydrolase. We evaluate each method's effectiveness in identifying the dominant binding mode and finding any additional binding modes (if any). Then, we compare our predicted binding modes to experimentally obtained X-ray crystal structures.</div><div>We dock each of the 12 small molecules into the apo-protein crystal structure and then run simulations up to 1 microsecond each. Small and fragment-like molecules likely have smaller energy barriers separating different binding modes by virtue of relatively fewer and weaker interactions relative to drug-like molecules, and thus likely undergo more rapid binding mode transitions. We expect, thus, to see more rapid transitions betweeen binding modes in our study. </div><div><br></div><div>Following this, we build Markov State Models (MSM) to define our stable ligand binding modes. We investigate if adequate sampling of ligand binding modes and transitions between them can occur at the microsecond timescale using traditional MD or a hybrid NCMC+MD simulation approach. Our findings suggest that even with small fragment-like molecules, we fail to sample all the crystallographic binding modes using microsecond MD simulations, but using NCMC+MD we have better success in sampling the crystal structure while obtaining the correct populations.</div>

scholarly journals The impact of cross-docked poses on performance of machine learning classifier for protein–ligand binding pose prediction

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Chao Shen ◽  
Xueping Hu ◽  
Junbo Gao ◽  
Xujun Zhang ◽  
Haiyang Zhong ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ligand Binding ◽  
Detailed Knowledge ◽  
Pose Prediction ◽  
Scoring Functions ◽  
Link Type ◽  
Cross Docking ◽  
Calculation Results ◽  
The Cross ◽  
The Impact

AbstractStructure-based drug design depends on the detailed knowledge of the three-dimensional (3D) structures of protein–ligand binding complexes, but accurate prediction of ligand-binding poses is still a major challenge for molecular docking due to deficiency of scoring functions (SFs) and ignorance of protein flexibility upon ligand binding. In this study, based on a cross-docking dataset dedicatedly constructed from the PDBbind database, we developed several XGBoost-trained classifiers to discriminate the near-native binding poses from decoys, and systematically assessed their performance with/without the involvement of the cross-docked poses in the training/test sets. The calculation results illustrate that using Extended Connectivity Interaction Features (ECIF), Vina energy terms and docking pose ranks as the features can achieve the best performance, according to the validation through the random splitting or refined-core splitting and the testing on the re-docked or cross-docked poses. Besides, it is found that, despite the significant decrease of the performance for the threefold clustered cross-validation, the inclusion of the Vina energy terms can effectively ensure the lower limit of the performance of the models and thus improve their generalization capability. Furthermore, our calculation results also highlight the importance of the incorporation of the cross-docked poses into the training of the SFs with wide application domain and high robustness for binding pose prediction. The source code and the newly-developed cross-docking datasets can be freely available at https://github.com/sc8668/ml_pose_prediction and https://zenodo.org/record/5525936, respectively, under an open-source license. We believe that our study may provide valuable guidance for the development and assessment of new machine learning-based SFs (MLSFs) for the predictions of protein–ligand binding poses.

Fragment Pose Prediction Using Non-Equilibrium Candidate Monte Carlo and Molecular Dynamics Simulations

2019 ◽  
Author(s):  
Nathan M. Lim ◽  
Meghan Osato ◽  
Gregory L. Warren ◽  
David L. Mobley
Keyword(s):  
Crystal Structure ◽  
Molecular Dynamics ◽  
Monte Carlo ◽  
Ligand Binding ◽  
Md Simulations ◽  
Binding Mode ◽  
Protein Crystal ◽  
Pose Prediction ◽  
Binding Modes ◽  
Non Equilibrium

<div>Part of early stage drug discovery involves determining how molecules may bind to the target protein. Through understanding where and how molecules bind, chemists can begin to build ideas on how to design improvements to increase binding affinities. In this retrospective study, we compare how computational approaches like docking, molecular dynamics (MD) simulations, and a non-equilibrium candidate Monte Carlo (NCMC) based method (NCMC+MD) perform in predicting binding modes for a set of 12 fragment-like molecules which bind to soluble epoxide hydrolase. We evaluate each method's effectiveness in identifying the dominant binding mode and finding any additional binding modes (if any). Then, we compare our predicted binding modes to experimentally obtained X-ray crystal structures.</div><div>We dock each of the 12 small molecules into the apo-protein crystal structure and then run simulations up to 1 microsecond each. Small and fragment-like molecules likely have smaller energy barriers separating different binding modes by virtue of relatively fewer and weaker interactions relative to drug-like molecules, and thus likely undergo more rapid binding mode transitions. We expect, thus, to see more rapid transitions betweeen binding modes in our study. </div><div><br></div><div>Following this, we build Markov State Models (MSM) to define our stable ligand binding modes. We investigate if adequate sampling of ligand binding modes and transitions between them can occur at the microsecond timescale using traditional MD or a hybrid NCMC+MD simulation approach. Our findings suggest that even with small fragment-like molecules, we fail to sample all the crystallographic binding modes using microsecond MD simulations, but using NCMC+MD we have better success in sampling the crystal structure while obtaining the correct populations.</div>

Elucidating the Multiple Roles of Hydration in Protein-Ligand Binding via Layerwise Relevance Propagation and Big Data Analytics

2019 ◽  
Author(s):  
Markus Lill ◽  
Ying Yang ◽  
Amr Mahmoud ◽  
Matthew Masters
Keyword(s):  
Big Data ◽  
Ligand Binding ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Computational Method ◽  
Correct Prediction ◽  
Pose Prediction ◽  
Learning Approaches ◽  
Protein Targets ◽  
Trained Neural Network

Hydration is a key player in protein-ligand association. No computational method for modeling hydration has so far consistently improved the scoring performance of docking approaches. Using molecular dynamics on thousands of proteins in conjunction with modern deep learning approaches allowed the successful modeling of hydration during scoring of protein-ligand binding poses. This on-the-fly inclusion of hydration information resulted in unprecedented accuracy in binding pose prediction.<br>Big-data analytics based on relevance deduced from the trained neural network<br>revealed that the correct prediction of binding poses depends on three essential pillars of hydration, i.e. water-mediated interactions, desolvation, and enthalpically stable water layers around the bound ligand. The latter form of hydration may open new avenues for optimizing ligands for diverse protein targets.

scholarly journals Cross‐docking benchmark for automated pose and ranking prediction of ligand binding

2019 ◽  
Vol 29 (1) ◽  
pp. 298-305 ◽  
Author(s):  
Shayne D. Wierbowski ◽  
Bentley M. Wingert ◽  
Jim Zheng ◽  
Carlos J. Camacho
Keyword(s):  
Ligand Binding ◽  
Cross Docking ◽  
Docking Benchmark

Fragment Pose Prediction Using Non-Equilibrium Candidate Monte Carlo and Molecular Dynamics Simulations

2019 ◽  
Author(s):  
Nathan M. Lim ◽  
Meghan Osato ◽  
Gregory L. Warren ◽  
David L. Mobley
Keyword(s):  
Crystal Structure ◽  
Molecular Dynamics ◽  
Monte Carlo ◽  
Ligand Binding ◽  
Md Simulations ◽  
Binding Mode ◽  
Protein Crystal ◽  
Pose Prediction ◽  
Binding Modes ◽  
Non Equilibrium

<div>Part of early stage drug discovery involves determining how molecules may bind to the target protein. Through understanding where and how molecules bind, chemists can begin to build ideas on how to design improvements to increase binding affinities. In this retrospective study, we compare how computational approaches like docking, molecular dynamics (MD) simulations, and a non-equilibrium candidate Monte Carlo (NCMC) based method (NCMC+MD) perform in predicting binding modes for a set of 12 fragment-like molecules which bind to soluble epoxide hydrolase. We evaluate each method's effectiveness in identifying the dominant binding mode and finding any additional binding modes (if any). Then, we compare our predicted binding modes to experimentally obtained X-ray crystal structures.</div><div>We dock each of the 12 small molecules into the apo-protein crystal structure and then run simulations up to 1 microsecond each. Small and fragment-like molecules likely have smaller energy barriers separating different binding modes by virtue of relatively fewer and weaker interactions relative to drug-like molecules, and thus likely undergo more rapid binding mode transitions. We expect, thus, to see more rapid transitions betweeen binding modes in our study. </div><div><br></div><div>Following this, we build Markov State Models (MSM) to define our stable ligand binding modes. We investigate if adequate sampling of ligand binding modes and transitions between them can occur at the microsecond timescale using traditional MD or a hybrid NCMC+MD simulation approach. Our findings suggest that even with small fragment-like molecules, we fail to sample all the crystallographic binding modes using microsecond MD simulations, but using NCMC+MD we have better success in sampling the crystal structure while obtaining the correct populations.</div>

Elucidating the Multiple Roles of Hydration in Protein-Ligand Binding via Layerwise Relevance Propagation and Big Data Analytics

2019 ◽  
Author(s):  
Markus Lill ◽  
Ying Yang ◽  
Amr Mahmoud ◽  
Matthew Masters
Keyword(s):  
Big Data ◽  
Ligand Binding ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Computational Method ◽  
Correct Prediction ◽  
Pose Prediction ◽  
Learning Approaches ◽  
Protein Targets ◽  
Trained Neural Network

Hydration is a key player in protein-ligand association. No computational method for modeling hydration has so far consistently improved the scoring performance of docking approaches. Using molecular dynamics on thousands of proteins in conjunction with modern deep learning approaches allowed the successful modeling of hydration during scoring of protein-ligand binding poses. This on-the-fly inclusion of hydration information resulted in unprecedented accuracy in binding pose prediction.<br>Big-data analytics based on relevance deduced from the trained neural network<br>revealed that the correct prediction of binding poses depends on three essential pillars of hydration, i.e. water-mediated interactions, desolvation, and enthalpically stable water layers around the bound ligand. The latter form of hydration may open new avenues for optimizing ligands for diverse protein targets.

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