scholarly journals Receptor flexibility in molecular cross-docking

Author(s):  
Lucia Sessa ◽  
Luigi Di BIasi ◽  
Rosaura Parisi ◽  
Simona Concilio ◽  
Stefano Piotto

Motivation Molecular docking is an efficient method to predict the conformations adopted by the ligand within the target binding site. Usually, standard docking protocol involves only one structure to represent the receptor, overlooking the changes in the binding pocket geometry induced by ligand binding. In our previous work, we observed that different conformations of the same target show different volume and shape of the internal cavities (Sessa et al., 2016). Different ligands may stabilize different receptor conformations with different internal cavities. Consequently, the crystallographic data represent the adaptation of a protein to a particular ligand. Cross-docking is a validation procedure consisting in docking a series of ligands into different conformation of the same receptor. Since the structures of the same receptor can be rather different, the cross-docking analyses are typically very poor. In these cases the internal cavity of the buried binding pocket does not have space enough to accommodate all ligands and this can radically affect the outcome and alter the cross-docking results. The changes of the cavity volume might explain the failure of traditional docking method and support the hypothesis that a single representative structure for the receptor is not enough. Keeping target proteins flexible during the docking has a high computational cost. To overcome this limit, our docking strategy is to represent receptor flexibility through an inexpensive method that generates a series of target structures. Starting from a known target structure, we used the molecular dynamics (MD) simulations to explore the conformational changes induced by ligand binding and to collect several snapshots of receptor structures to perform the cross-docking studies. To validate the accuracy of our flexible protocol in docking, we used a set of 10 crystallographic conformations of Androgen Receptor with the same target but with a different ligand. We performed two parallel experiments of docking, one with a rigid protein target and one considering flexible receptor structures. In addition, we compared the results for both experiments in the re-docking and in the cross-docking analysis. Methods Ten receptor structures complexed with a ligand were extracted from the X-ray structures in the PDB database (Berman et al., 2000). Several conformations for each receptor were selected from the molecular dynamics simulations (MD) at regular time intervals (each 500 ps). The MD simulations were performed with the software YASARA Structure 16.2.14 (Krieger & Vriend, 2014) using AMBER14 as force field. The molecular docking simulations were performed using VINA provided in the YASARA package. "Abstract truncated at 3,000 characters - the full version is available in the pdf file"

2016 ◽  
Author(s):  
Lucia Sessa ◽  
Luigi Di BIasi ◽  
Rosaura Parisi ◽  
Simona Concilio ◽  
Stefano Piotto

Motivation Molecular docking is an efficient method to predict the conformations adopted by the ligand within the target binding site. Usually, standard docking protocol involves only one structure to represent the receptor, overlooking the changes in the binding pocket geometry induced by ligand binding. In our previous work, we observed that different conformations of the same target show different volume and shape of the internal cavities (Sessa et al., 2016). Different ligands may stabilize different receptor conformations with different internal cavities. Consequently, the crystallographic data represent the adaptation of a protein to a particular ligand. Cross-docking is a validation procedure consisting in docking a series of ligands into different conformation of the same receptor. Since the structures of the same receptor can be rather different, the cross-docking analyses are typically very poor. In these cases the internal cavity of the buried binding pocket does not have space enough to accommodate all ligands and this can radically affect the outcome and alter the cross-docking results. The changes of the cavity volume might explain the failure of traditional docking method and support the hypothesis that a single representative structure for the receptor is not enough. Keeping target proteins flexible during the docking has a high computational cost. To overcome this limit, our docking strategy is to represent receptor flexibility through an inexpensive method that generates a series of target structures. Starting from a known target structure, we used the molecular dynamics (MD) simulations to explore the conformational changes induced by ligand binding and to collect several snapshots of receptor structures to perform the cross-docking studies. To validate the accuracy of our flexible protocol in docking, we used a set of 10 crystallographic conformations of Androgen Receptor with the same target but with a different ligand. We performed two parallel experiments of docking, one with a rigid protein target and one considering flexible receptor structures. In addition, we compared the results for both experiments in the re-docking and in the cross-docking analysis. Methods Ten receptor structures complexed with a ligand were extracted from the X-ray structures in the PDB database (Berman et al., 2000). Several conformations for each receptor were selected from the molecular dynamics simulations (MD) at regular time intervals (each 500 ps). The MD simulations were performed with the software YASARA Structure 16.2.14 (Krieger & Vriend, 2014) using AMBER14 as force field. The molecular docking simulations were performed using VINA provided in the YASARA package. "Abstract truncated at 3,000 characters - the full version is available in the pdf file"


2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Andrew T. McNutt ◽  
Paul Francoeur ◽  
Rishal Aggarwal ◽  
Tomohide Masuda ◽  
Rocco Meli ◽  
...  

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.


2021 ◽  
Vol 12 ◽  
Author(s):  
Trina Ekawati Tallei ◽  
Fatimawali ◽  
Afriza Yelnetty ◽  
Rinaldi Idroes ◽  
Diah Kusumawaty ◽  
...  

The rapid spread of a novel coronavirus known as SARS-CoV-2 has compelled the entire world to seek ways to weaken this virus, prevent its spread and also eliminate it. However, no drug has been approved to treat COVID-19. Furthermore, the receptor-binding domain (RBD) on this viral spike protein, as well as several other important parts of this virus, have recently undergone mutations, resulting in new virus variants. While no treatment is currently available, a naturally derived molecule with known antiviral properties could be used as a potential treatment. Bromelain is an enzyme found in the fruit and stem of pineapples. This substance has been shown to have a broad antiviral activity. In this article, we analyse the ability of bromelain to counteract various variants of the SARS-CoV-2 by targeting bromelain binding on the side of this viral interaction with human angiotensin-converting enzyme 2 (hACE2) using molecular docking and molecular dynamics simulation approaches. We have succeeded in making three-dimensional configurations of various RBD variants using protein modelling. Bromelain exhibited good binding affinity toward various variants of RBDs and binds right at the binding site between RBDs and hACE2. This result is also presented in the modelling between Bromelain, RBD, and hACE2. The molecular dynamics (MD) simulations study revealed significant stability of the bromelain and RBD proteins separately up to 100 ns with an RMSD value of 2 Å. Furthermore, despite increases in RMSD and changes in Rog values of complexes, which are likely due to some destabilized interactions between bromelain and RBD proteins, two proteins in each complex remained bonded, and the site where the two proteins bind remained unchanged. This finding indicated that bromelain could have an inhibitory effect on different SARS-CoV-2 variants, paving the way for a new SARS-CoV-2 inhibitor drug. However, more in vitro and in vivo research on this potential mechanism of action is required.


Author(s):  
S. Wu ◽  
P. Angelikopoulos ◽  
C. Papadimitriou ◽  
R. Moser ◽  
P. Koumoutsakos

We present a hierarchical Bayesian framework for the selection of force fields in molecular dynamics (MD) simulations. The framework associates the variability of the optimal parameters of the MD potentials under different environmental conditions with the corresponding variability in experimental data. The high computational cost associated with the hierarchical Bayesian framework is reduced by orders of magnitude through a parallelized Transitional Markov Chain Monte Carlo method combined with the Laplace Asymptotic Approximation. The suitability of the hierarchical approach is demonstrated by performing MD simulations with prescribed parameters to obtain data for transport coefficients under different conditions, which are then used to infer and evaluate the parameters of the MD model. We demonstrate the selection of MD models based on experimental data and verify that the hierarchical model can accurately quantify the uncertainty across experiments; improve the posterior probability density function estimation of the parameters, thus, improve predictions on future experiments; identify the most plausible force field to describe the underlying structure of a given dataset. The framework and associated software are applicable to a wide range of nanoscale simulations associated with experimental data with a hierarchical structure.


Author(s):  
Peyman Honarmandi ◽  
Philip Bransford ◽  
Roger D. Kamm

Mechanical properties of biomolecules and their response to mechanical forces may be studied using Molecular Dynamics (MD) simulations. However, high computational cost is a primary drawback of MD simulations. This paper presents a computational framework based on the integration of the Finite Element Method (FEM) with MD simulations to calculate the mechanical properties of polyalanine α-helix proteins. In this method, proteins are treated as continuum elastic solids with molecular volume defined exclusively by their atomic surface. Therefore, all solid mechanics theories would be applicable for the presumed elastic media. All-atom normal mode analysis is used to calculate protein’s elastic stiffness as input to the FEM. In addition, constant force molecular dynamics (CFMD) simulations can be used to predict other effective mechanical properties, such as the Poisson’s Ratio. Force versus strain data help elucidate the mechanical behavior of α-helices upon application of constant load. The proposed method may be useful in identifying the mechanical properties of any protein or protein assembly with known atomic structure.


RSC Advances ◽  
2019 ◽  
Vol 9 (45) ◽  
pp. 26176-26208 ◽  
Author(s):  
Manoj G. Damale ◽  
Rajesh B. Patil ◽  
Siddique Akber Ansari ◽  
Hamad M. Alkahtani ◽  
Abdulrahman A. Almehizia ◽  
...  

Computational approaches such as pharmacophore modeling, virtual screening and MD simulations were explored to find the potential hits as H. pylori specific panC inhibitors for the management of gastric ulcers and gastric cancers.


2020 ◽  
Vol 36 (18) ◽  
pp. 4714-4720
Author(s):  
Farzin Sohraby ◽  
Mostafa Javaheri Moghadam ◽  
Masoud Aliyar ◽  
Hassan Aryapour

Abstract Summary Small molecules such as metabolites and drugs play essential roles in biological processes and pharmaceutical industry. Knowing their interactions with biomacromolecular targets demands a deep understanding of binding mechanisms. Dozens of papers have suggested that discovering of the binding event by means of conventional unbiased molecular dynamics (MD) simulation urges considerable amount of computational resources, therefore, only one who holds a cluster or a supercomputer can afford such extensive simulations. Thus, many researchers who do not own such resources are reluctant to take the benefits of running unbiased MD simulation, in full atomistic details, when studying a ligand binding pathway. Many researchers are impelled to be content with biased MD simulations which seek its validation due to its intrinsic preconceived framework. In this work, we have presented a workable stratagem to encourage everyone to perform unbiased (unguided) MD simulations, in this case a protein–ligand binding process, by typical desktop computers and so achieve valuable results in nanosecond time scale. Here, we have described a dynamical binding’s process of an anticancer drug, the dasatinib, to the c-Src kinase in full atomistic details for the first time, without applying any biasing force or potential which may lead the drug to artificial interactions with the protein. We have attained multiple independent binding events which occurred in the nanosecond time scales, surprisingly as little as ∼30 ns. Both the protonated and deprotonated forms of the dasatinib reached the crystallographic binding mode without having any major intermediate state during induction. Availability and implementation The links of the tutorial and technical documents are accessible in the article. Supplementary information Supplementary data are available at Bioinformatics online.


RSC Advances ◽  
2018 ◽  
Vol 8 (24) ◽  
pp. 13310-13322 ◽  
Author(s):  
Saša Kazazić ◽  
Zrinka Karačić ◽  
Igor Sabljić ◽  
Dejan Agić ◽  
Marko Tomin ◽  
...  

The hydrogen deuterium exchange (HDX) mass spectrometry combined with molecular dynamics (MD) simulations was employed to investigate conformational dynamics and ligand binding within the M49 family (dipeptidyl peptidase III family).


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