scholarly journals Sampling Conformational Changes of Bound Ligands Using Nonequilibrium Candidate Monte Carlo

2019 ◽  
Author(s):  
Sukanya Sasmal ◽  
Samuel C. Gill ◽  
Nathan M. Lim ◽  
David Mobley
Keyword(s):  
Monte Carlo ◽  
Conformational Changes ◽  
Population Distribution ◽  
Computational Cost ◽  
Binding Pocket ◽  
Umbrella Sampling ◽  
Binding Modes ◽  
Flexible Part ◽  
Acceptance Rates ◽  
Flexible Ligands

Flexible ligands often have multiple binding modes or bound conformations that differ by rotation of a portion of the molecule around internal rotatable bonds. Knowledge of these binding modes is important for understanding the interactions stabilizing the ligand in the binding pocket, and also for calculating accurate binding affinities. In this work, we use a hybrid molecular dynamics (MD)/non-equilibrium candidate Monte Carlo (NCMC) method to sample the different binding modes of several flexible ligands and also to estimate the population distribution of the modes. The NCMC move proposal is divided into three parts. The flexible part of the ligand is alchemically turned off by decreasing the electrostatics and steric interactions gradually, followed by rotating the rotatable bond by a random angle and then slowly turning the ligand back on to its fully interacting state. The alchemical steps prior to and after the move proposal help the surrounding protein and water atoms in the binding pocket relax around the proposed ligand conformation and increase move acceptance rates. The protein-ligand system is propagated using classical MD in between the NCMC proposals. Using this MD/NCMC method, we were able to correctly reproduce the different binding modes of inhibitors binding to two kinase targets -- c-Jun N-terminal kinase-1 and cyclin-dependent kinase 2 -- at a much lower computational cost compared to conventional MD and umbrella sampling. This method is available as a part of the BLUES software package.

scholarly journals Sampling Conformational Changes of Bound Ligands Using Nonequilibrium Candidate Monte Carlo

2019 ◽  
Author(s):  
Sukanya Sasmal ◽  
Samuel C. Gill ◽  
Nathan M. Lim ◽  
David Mobley
Keyword(s):  
Monte Carlo ◽  
Conformational Changes ◽  
Population Distribution ◽  
Computational Cost ◽  
Binding Pocket ◽  
Umbrella Sampling ◽  
Binding Modes ◽  
Flexible Part ◽  
Acceptance Rates ◽  
Flexible Ligands

Flexible ligands often have multiple binding modes or bound conformations that differ by rotation of a portion of the molecule around internal rotatable bonds. Knowledge of these binding modes is important for understanding the interactions stabilizing the ligand in the binding pocket, and also for calculating accurate binding affinities. In this work, we use a hybrid molecular dynamics (MD)/non-equilibrium candidate Monte Carlo (NCMC) method to sample the different binding modes of several flexible ligands and also to estimate the population distribution of the modes. The NCMC move proposal is divided into three parts. The flexible part of the ligand is alchemically turned off by decreasing the electrostatics and steric interactions gradually, followed by rotating the rotatable bond by a random angle and then slowly turning the ligand back on to its fully interacting state. The alchemical steps prior to and after the move proposal help the surrounding protein and water atoms in the binding pocket relax around the proposed ligand conformation and increase move acceptance rates. The protein-ligand system is propagated using classical MD in between the NCMC proposals. Using this MD/NCMC method, we were able to correctly reproduce the different binding modes of inhibitors binding to two kinase targets -- c-Jun N-terminal kinase-1 and cyclin-dependent kinase 2 -- at a much lower computational cost compared to conventional MD and umbrella sampling. This method is available as a part of the BLUES software package.

scholarly journals Molecular determinants of the interactions between proteins and ssDNA

2015 ◽  
Vol 112 (16) ◽  
pp. 5033-5038 ◽  
Author(s):  
Garima Mishra ◽  
Yaakov Levy
Keyword(s):  
Conformational Changes ◽  
Protein Interactions ◽  
Binding Pocket ◽  
Dna Bases ◽  
Coarse Grained ◽  
Binding Modes ◽  
Ssdna Binding ◽  
Coarse Grained Model ◽  
Molecular Determinants ◽  
Ssdna Binding Proteins

ssDNA binding proteins (SSBs) protect ssDNA from chemical and enzymatic assault that can derail DNA processing machinery. Complexes between SSBs and ssDNA are often highly stable, but predicting their structures is challenging, mostly because of the inherent flexibility of ssDNA and the geometric and energetic complexity of the interfaces that it forms. Here, we report a newly developed coarse-grained model to predict the structure of SSB–ssDNA complexes. The model is successfully applied to predict the binding modes of six SSBs with ssDNA strands of lengths of 6–65 nt. In addition to charge–charge interactions (which are often central to governing protein interactions with nucleic acids by means of electrostatic complementarity), an essential energetic term to predict SSB–ssDNA complexes is the interactions between aromatic residues and DNA bases. For some systems, flexibility is required from not only the ssDNA but also, the SSB to allow it to undergo conformational changes and the penetration of the ssDNA into its binding pocket. The association mechanisms can be quite varied, and in several cases, they involve the ssDNA sliding along the protein surface. The binding mechanism suggests that coarse-grained models are appropriate to study the motion of SSBs along ssDNA, which is expected to be central to the function carried out by the SSBs.

Reversibly Sampling Conformations and Binding Modes Using Molecular Darting

2020 ◽  
Author(s):  
Samuel C. Gill ◽  
David Mobley
Keyword(s):  
Monte Carlo ◽  
Ligand Binding ◽  
Binding Sites ◽  
Degrees Of Freedom ◽  
Dynamics Simulation ◽  
Hiv Integrase ◽  
Binding Modes ◽  
System A ◽  
Multiple Binding ◽  
Internal Degrees Of Freedom

<div>Sampling multiple binding modes of a ligand in a single molecular dynamics simulation is difficult. A given ligand may have many internal degrees of freedom, along with many different ways it might orient itself a binding site or across several binding sites, all of which might be separated by large energy barriers. We have developed a novel Monte Carlo move called Molecular Darting (MolDarting) to reversibly sample between predefined binding modes of a ligand. Here, we couple this with nonequilibrium candidate Monte Carlo (NCMC) to improve acceptance of moves.</div><div>We apply this technique to a simple dipeptide system, a ligand binding to T4 Lysozyme L99A, and ligand binding to HIV integrase in order to test this new method. We observe significant increases in acceptance compared to uniformly sampling the internal, and rotational/translational degrees of freedom in these systems.</div>

Binding Modes of Ligands Using Enhanced Sampling (BLUES): Rapid Decorrelation of Ligand Binding Modes Using Nonequilibrium Candidate Monte Carlo

2017 ◽  
Author(s):  
Samuel Gill ◽  
Nathan M. Lim ◽  
Patrick Grinaway ◽  
Ariën S. Rustenburg ◽  
Josh Fass ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Ligand Binding ◽  
Binding Mode ◽  
Free Energy Calculations ◽  
Dynamics Simulation ◽  
Binding Modes ◽  
Enhanced Sampling ◽  
Recent Success ◽  
Multiple Binding ◽  
Proper Sampling

<div>Accurately predicting protein-ligand binding is a major goal in computational chemistry, but even the prediction of ligand binding modes in proteins poses major challenges. Here, we focus on solving the binding mode prediction problem for rigid fragments. That is, we focus on computing the dominant placement, conformation, and orientations of a relatively rigid, fragment-like ligand in a receptor, and the populations of the multiple binding modes which may be relevant. This problem is important in its own right, but is even more timely given the recent success of alchemical free energy calculations. Alchemical calculations are increasingly used to predict binding free energies of ligands to receptors. However, the accuracy of these calculations is dependent on proper sampling of the relevant ligand binding modes. Unfortunately, ligand binding modes may often be uncertain, hard to predict, and/or slow to interconvert on simulation timescales, so proper sampling with current techniques can require prohibitively long simulations. We need new methods which dramatically improve sampling of ligand binding modes. Here, we develop and apply a nonequilibrium candidate Monte Carlo (NCMC) method to improve sampling of ligand binding modes.</div><div><br></div><div>In this technique the ligand is rotated and subsequently allowed to relax in its new position through alchemical perturbation before accepting or rejecting the rotation and relaxation as a nonequilibrium Monte Carlo move. When applied to a T4 lysozyme model binding system, this NCMC method shows over two orders of magnitude improvement in binding mode sampling efficiency compared to a brute force molecular dynamics simulation. This is a first step towards applying this methodology to pharmaceutically relevant binding of fragments and, eventually, drug-like molecules. We are making this approach available via our new Binding Modes of Ligands using Enhanced Sampling (BLUES) package which is freely available on GitHub.</div>

Binding Modes of Ligands Using Enhanced Sampling (BLUES): Rapid Decorrelation of Ligand Binding Modes Using Nonequilibrium Candidate Monte Carlo

2018 ◽  
Author(s):  
Samuel Gill ◽  
Nathan M. Lim ◽  
Patrick Grinaway ◽  
Ariën S. Rustenburg ◽  
Josh Fass ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Ligand Binding ◽  
Binding Mode ◽  
Free Energy Calculations ◽  
Dynamics Simulation ◽  
Binding Modes ◽  
Enhanced Sampling ◽  
Recent Success ◽  
Multiple Binding ◽  
Proper Sampling

<div>Accurately predicting protein-ligand binding is a major goal in computational chemistry, but even the prediction of ligand binding modes in proteins poses major challenges. Here, we focus on solving the binding mode prediction problem for rigid fragments. That is, we focus on computing the dominant placement, conformation, and orientations of a relatively rigid, fragment-like ligand in a receptor, and the populations of the multiple binding modes which may be relevant. This problem is important in its own right, but is even more timely given the recent success of alchemical free energy calculations. Alchemical calculations are increasingly used to predict binding free energies of ligands to receptors. However, the accuracy of these calculations is dependent on proper sampling of the relevant ligand binding modes. Unfortunately, ligand binding modes may often be uncertain, hard to predict, and/or slow to interconvert on simulation timescales, so proper sampling with current techniques can require prohibitively long simulations. We need new methods which dramatically improve sampling of ligand binding modes. Here, we develop and apply a nonequilibrium candidate Monte Carlo (NCMC) method to improve sampling of ligand binding modes.</div><div><br></div><div>In this technique the ligand is rotated and subsequently allowed to relax in its new position through alchemical perturbation before accepting or rejecting the rotation and relaxation as a nonequilibrium Monte Carlo move. When applied to a T4 lysozyme model binding system, this NCMC method shows over two orders of magnitude improvement in binding mode sampling efficiency compared to a brute force molecular dynamics simulation. This is a first step towards applying this methodology to pharmaceutically relevant binding of fragments and, eventually, drug-like molecules. We are making this approach available via our new Binding Modes of Ligands using Enhanced Sampling (BLUES) package which is freely available on GitHub.</div>

Application of Molecular Docking for the Development of Improved HIV-1 Reverse Transcriptase Inhibitors

2020 ◽  
Vol 16 ◽  
Author(s):  
Arash Soltani ◽  
Seyed Isaac Hashemy ◽  
Farnaz Zahedi Avval ◽  
Houshang Rafatpanah ◽  
Seyed Abdolrahim Rezaee ◽  
...  
Keyword(s):  
Reverse Transcriptase ◽  
Binding Capacity ◽  
Binding Pocket ◽  
Ligand Docking ◽  
Binding Modes ◽  
Wild Type ◽  
Parent Molecule ◽  
Discovery Studio ◽  
Approved Drugs ◽  
Hiv 1

Introoduction: Inhibition of the reverse transcriptase (RT) enzyme of human immunodeficiency virus (HIV) by low molecular weight inhibitors is still an active area of research. Here, protein-ligand interactions and possible binding modes of novel compounds with the HIV-1 RT binding pocket (the wild-type as well as Y181C and K103N mutants) were obtained and discussed. Methods: A molecular fragment-based approach using FDA-approved drugs were followed to design novel chemical derivatives using delavirdine, efavirenz, etravirine and rilpivirine as the scaffolds. The drug-likeliness of the derivatives was evaluated using Swiss-ADME. Then the parent molecule and derivatives were docked into the binding pocket of related crystal structures (PDB ID: 4G1Q, 1IKW, 1KLM and 3MEC). Genetic Optimization for Ligand Docking (GOLD) Suite 5.2.2 software was used for docking and the results analyzed in the Discovery Studio Visualizer 4. A derivative was chosen for further analysis, if it passed drug-likeliness and the docked energy was more favorable than that of its parent molecule. Out of the fifty-seven derivatives, forty-eight failed in druglikeness screening by Swiss-ADME or in docking stage. Results: The final results showed that the selected compounds had higher predicted binding affinities than their parent scaffolds in both wild-type and the mutants. Binding energy improvement was higher for the structures designed based on second-generation NNRTIs (etravirine and rilpivirine) than the first-generation NNRTIs (delavirdine and efavirenz). For example, while the docked energy for rilpivirine was -51 KJ/mol, it was improved for its derivatives RPV01 and RPV15 up to -58.3 and -54.5 KJ/mol, respectively. Conclusion: In this study, we have identified and proposed some novel molecules with improved binding capacity for HIV RT using fragment-based approach.

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.

Multilevel Monte Carlo by using the Halton sequence

2020 ◽  
Vol 26 (3) ◽  
pp. 193-203
Author(s):  
Shady Ahmed Nagy ◽  
Mohamed A. El-Beltagy ◽  
Mohamed Wafa
Keyword(s):  
Monte Carlo ◽  
Differential Equations ◽  
Stochastic Differential Equations ◽  
Computational Cost ◽  
Random Numbers ◽  
Multilevel Monte Carlo ◽  
Halton Sequence ◽  
Random Generator ◽  
Mc Simulation ◽  
Multilevel Monte Carlo Method

AbstractMonte Carlo (MC) simulation depends on pseudo-random numbers. The generation of these numbers is examined in connection with the Brownian motion. We present the low discrepancy sequence known as Halton sequence that generates different stochastic samples in an equally distributed form. This will increase the convergence and accuracy using the generated different samples in the Multilevel Monte Carlo method (MLMC). We compare algorithms by using a pseudo-random generator and a random generator depending on a Halton sequence. The computational cost for different stochastic differential equations increases in a standard MC technique. It will be highly reduced using a Halton sequence, especially in multiplicative stochastic differential equations.

scholarly journals Effect of ATP Binding and Hydrolysis on Dynamics of Canine Parvovirus NS1

2010 ◽  
Vol 84 (10) ◽  
pp. 5391-5403 ◽  
Author(s):  
Einari A. Niskanen ◽  
Teemu O. Ihalainen ◽  
Olli Kalliolinna ◽  
Milla M. Häkkinen ◽  
Maija Vihinen-Ranta
Keyword(s):  
Binding Pocket ◽  
Canine Parvovirus ◽  
Dominant Negative ◽  
Atp Binding ◽  
Helicase Domain ◽  
Genome Replication ◽  
Binding Modes ◽  
Ns1 Protein ◽  
Sensory Element ◽  
Arginine Finger

ABSTRACT The replication protein NS1 is essential for genome replication and protein production in parvoviral infection. Many of its functions, including recognition and site-specific nicking of the viral genome, helicase activity, and transactivation of the viral capsid promoter, are dependent on ATP. An ATP-binding pocket resides in the middle of the modular NS1 protein in a superfamily 3 helicase domain. Here we have identified key ATP-binding amino acid residues in canine parvovirus (CPV) NS1 protein and mutated amino acids from the conserved A motif (K406), B motif (E444 and E445), and positively charged region (R508 and R510). All mutations prevented the formation of infectious viruses. When provided in trans, all except the R508A mutation reduced infectivity in a dominant-negative manner, possibly by hindering genome replication. These results suggest that the conserved R510 residue, but not R508, is the arginine finger sensory element of CPV NS1. Moreover, fluorescence recovery after photobleaching (FRAP), complemented by computer simulations, was used to assess the binding properties of mutated fluorescent fusion proteins. These experiments identified ATP-dependent and -independent binding modes for NS1 in living cells. Only the K406M mutant had a single binding site, which was concluded to indicate ATP-independent binding. Furthermore, our data suggest that DNA binding of NS1 is dependent on its ability to both bind and hydrolyze ATP.

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