scholarly journals Discover, Sample and Refine: Exploring Chemistry with Enhanced Sampling Techniques

2021 ◽  
Author(s):  
Umberto Raucci ◽  
Valerio Rizzi ◽  
Michele Parrinello
Keyword(s):  
Free Energy ◽  
Ad Hoc ◽  
Spectral Graph Theory ◽  
Ab Initio Molecular Dynamics ◽  
Enhanced Sampling ◽  
Free Energy Surface ◽  
Collective Variables ◽  
User Input ◽  
Specific Reactions

Over the last few decades enhanced sampling methods have made great strides. Here, we exploit this progress and propose a modular workflow for blind reaction discovery and characterization of reaction paths. Central to our strategy is the use of the recently developed explore variant of the on-the-fly probability enhanced sampling method. Like metadynamics, this method is based on the identification of appropriate collective variables. Our first step is the discovery of new chemical reactions and it is performed biasing a one dimensional collective variable derived from spectral graph theory. Once new reaction pathways are detected, we construct ad-hoc tailored neural-network based collective variables to improve sampling of specific reactions and finally we refine the results using free energy perturbation theory. Our workflow has been successfully applied to both intramolecular and intermolecular reactions. Without any chemical hypothesis, we discovered several possible products, computed the free energy surface at semiempirical level, and finally refined it with a more accurate Hamiltonian. Our workflow requires minimal user input, and thanks to its modularity and flexibility, can extend the scope of ab initio molecular dynamics for the exploration and characterization of reaction space.

scholarly journals Free Energy Surfaces and Barriers for Vacancy Diffusion on Al(100), Al(110), Al(111) Reconstructed Surfaces

Nanomaterials ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 76
Author(s):  
Junais Habeeb Mokkath ◽  
Mufasila Mumthaz Muhammed ◽  
Ali J. Chamkha
Keyword(s):  
Free Energy ◽  
Energy Barrier ◽  
Computational Study ◽  
Metastable States ◽  
Vacancy Diffusion ◽  
Enhanced Sampling ◽  
Free Energy Surface ◽  
Collective Variables ◽  
Surface Vacancy ◽  
Energy Surfaces

Metadynamics is a popular enhanced sampling method based on the recurrent application of a history-dependent adaptive bias potential that is a function of a selected number of appropriately chosen collective variables. In this work, using metadynamics simulations, we performed a computational study for the diffusion of vacancies on three different Al surfaces [reconstructed Al(100), Al(110), and Al(111) surfaces]. We explored the free energy landscape of diffusion and estimated the barriers associated with this process on each surface. It is found that the surfaces are unique regarding vacancy diffusion. More specically, the reconstructed Al(110) surface presents four metastable states on the free energy surface having sizable and connected passage-ways with an energy barrier of height 0.55 eV. On the other hand, the reconstructed Al(100)/Al(111) surfaces exhibit two/three metastable states, respectively, with an energy barrier of height 0.33 eV. The findings in this study can help to understand surface vacancy diffusion in technologically relevant Al surfaces.

scholarly journals Potential Drug Candidates for SARS-CoV-2 Using Computational Screening and Enhanced Sampling Methods

2020 ◽  
Author(s):  
Sadanandam Namsani ◽  
Debabrata Pramanik ◽  
Mohd Aamir Khan ◽  
Sudip Roy ◽  
Jayant Singh
Keyword(s):  
Free Energy ◽  
High Performance ◽  
Search Space ◽  
Computational Method ◽  
Enhanced Sampling ◽  
Free Energy Surface ◽  
Drug Candidates ◽  
Drug Molecules ◽  
Computational Screening ◽  
Graphical Processing

<div><div><div><p>Here we report new chemical entities that are highly specific in binding towards the 3-chymotrypsin- like cysteine protease (3CLpro) protein present in the novel SARS-CoV2 virus. The viral 3CLpro</p><p>protein controls coronavirus replication. Therefore, 3CLpro is identified as a target for drug molecules. We have implemented an enhanced sampling method in combination with molecular dynamics and docking to bring down the computational screening search space to four molecules that could be synthesised and tested against COVID-19. Our computational method is much more robust than any other method available for drug screening e.g., docking, because of sampling of the free energy surface of the binding site of the protein (including the ligand) and use of explicit solvent. We have considered all possible interactions between all the atoms present in the protein, ligands, and water. Using high performance computing with graphical processing units we are able to perform large number of simulations within a month's time and converge to 4 most strongly bound ligands (by free energy and other scores) from a set of 17 ligands with lower docking scores. Based on our results and analysis, we claim with high confidence, that we have identified four potential ligands. Out of those, one particular ligand is the most promising candidate, based on free energy data, for further synthesis and testing against SARS-CoV-2 and might be effective for the cure of COVID-19.</p></div></div></div>

scholarly journals Neural networks-based variationally enhanced sampling

2019 ◽  
Vol 116 (36) ◽  
pp. 17641-17647 ◽  
Author(s):  
Luigi Bonati ◽  
Yue-Yu Zhang ◽  
Michele Parrinello
Keyword(s):  
Neural Networks ◽  
Free Energy ◽  
Atomistic Simulation ◽  
Sampling Method ◽  
Machine Learning Techniques ◽  
Enhanced Sampling ◽  
Collective Variables ◽  
Learning Techniques ◽  
Minimization Technique ◽  
Energy Surfaces

Sampling complex free-energy surfaces is one of the main challenges of modern atomistic simulation methods. The presence of kinetic bottlenecks in such surfaces often renders a direct approach useless. A popular strategy is to identify a small number of key collective variables and to introduce a bias potential that is able to favor their fluctuations in order to accelerate sampling. Here, we propose to use machine-learning techniques in conjunction with the recent variationally enhanced sampling method [O. Valsson, M. Parrinello, Phys. Rev. Lett. 113, 090601 (2014)] in order to determine such potential. This is achieved by expressing the bias as a neural network. The parameters are determined in a variational learning scheme aimed at minimizing an appropriate functional. This required the development of a more efficient minimization technique. The expressivity of neural networks allows representing rapidly varying free-energy surfaces, removes boundary effects artifacts, and allows several collective variables to be handled.

scholarly journals Evaluating the Efficiency of the Martini Force Field to Study Protein Dimerization in Aqueous and Membrane Environments

2021 ◽  
Author(s):  
Christos Lamprakis ◽  
Ioannis Andreadelis ◽  
John Manchester ◽  
Camilo Velez-Vega ◽  
José S. Duca ◽  
...  
Keyword(s):  
Free Energy ◽  
Membrane Proteins ◽  
Force Field ◽  
Biological Activities ◽  
Coarse Grained ◽  
Protein Oligomerization ◽  
Free Energy Surface ◽  
Collective Variables ◽  
Protein Dimerization ◽  
Dimerization Process

<p>Protein-protein complex assembly is one of the major drivers of biological response. Understanding the mechanisms of protein oligomerization/dimerization would allow one to elucidate how these complexes participate in biological activities and could ultimately lead to new approaches in designing novel therapeutic agents. However, determining the exact association pathways and structures of such complexes remains a challenge. Here, we use parallel tempering metadynamics simulations in the well-tempered ensemble to evaluate the performance of Martini 2.2P and Martini open-beta 3 (Martini 3) force fields in reproducing the structure and energetics of the dimerization process of membrane proteins and proteins in an aqueous solution in reasonable accuracy and throughput. We find that Martini 2.2P systematically overestimates the free energy of association by estimating large barriers in distinct areas, which likely leads to overaggregation when multiple monomers are present. In comparison, the less viscous Martini 3 results in a systematic underestimation of the free energy of association for proteins in solution, while it performs well in describing the association of membrane proteins. In all cases the near-native dimer complexes are identified as minima in the free energy surface albeit not always as the lowest minima. In the case of Martini 3 we find that the spurious supramolecular protein aggregation present in Martini 2.2P multimer simulations is alleviated and thus this force field may be more suitable for the study of protein oligomerization. We propose that the use of enhanced sampling simulations with a refined coarse-grained force field and appropriately defined collective variables is a robust approach for studying the protein dimerization process, although one should be cautious of the ranking of energy minima.</p>

scholarly journals Molecular dynamics simulations of liquid silica crystallization

2018 ◽  
Vol 115 (21) ◽  
pp. 5348-5352 ◽  
Author(s):  
Haiyang Niu ◽  
Pablo M. Piaggi ◽  
Michele Invernizzi ◽  
Michele Parrinello
Keyword(s):  
Free Energy ◽  
Nucleation Theory ◽  
Classical Nucleation Theory ◽  
Free Energy Surface ◽  
X Ray Diffraction ◽  
Collective Variables ◽  
Atomic Simulations ◽  
The Difference ◽  
Dynamics Simulations ◽  
Good Agreement

Silica is one of the most abundant minerals on Earth and is widely used in many fields. Investigating the crystallization of liquid silica by atomic simulations is of great importance to understand the crystallization mechanism; however, the high crystallization barrier and the tendency of silica to form glasses make such simulations very challenging. Here we have studied liquid silica crystallization to β-cristobalite with metadynamics, using X-ray diffraction (XRD) peak intensities as collective variables. The frequent transitions between solid and liquid of the biased runs demonstrate the highly successful use of the XRD peak intensities as collective variables, which leads to the convergence of the free-energy surface. By calculating the difference in free energy, we have estimated the melting temperature of β-cristobalite, which is in good agreement with the literature. The nucleation mechanism during the crystallization of liquid silica can be described by classical nucleation theory.

Potential Drug Candidates for SARS-CoV-2 Using Computational Screening and Enhanced Sampling Methods

2020 ◽  
Author(s):  
Sadanandam Namsani ◽  
Debabrata Pramanik ◽  
Mohd Aamir Khan ◽  
Sudip Roy ◽  
Jayant Singh
Keyword(s):  
Free Energy ◽  
High Performance ◽  
Specific Binding ◽  
Search Space ◽  
Computational Method ◽  
Enhanced Sampling ◽  
Free Energy Surface ◽  
Drug Candidates ◽  
Computational Screening ◽  
Experimental Crystal

<p>Here, we report new chemical entities that exhibit highly specific binding to the 3-chymotrypsin-like cysteine protease (3CLpro) present in the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Because the viral 3CLpro protein controls coronavirus replication, 3CLpro is identified as a target for drug molecules. We implemented an enhanced sampling method in combination with molecular dynamics and docking to reduce the computational screening search space to four molecules that could be synthesized and tested against SARS-CoV-2. Our computational method is much more robust than any other method available for drug screening (e.g., docking) because of sampling of the free energy surface of the binding site of the protein (including the ligand) and use of explicit solvent. We have considered all possible interactions between all the atoms present in the protein, ligands, and water. Using high-performance computing with graphical processing units, we were able to perform a large number of simulations within a month and converge the results to the four most strongly bound ligands (based on free energy and other scores) from a set of 17 ligands with lower docking scores. Additionally, we have considered N3 and 13b α-ketoamide inhibitors as controls for which experimental crystal structures are available. Out of the top four ligands, PI-06 was found to have a higher screening score compared to the controls. Based on our results and analysis, we confidently claim that we have identified four potential ligands, out of which one ligand is the best choice based on free energy and the most promising candidate for further synthesis and testing against SARS-CoV-2.<br></p>

scholarly journals Potential Drug Candidates for SARS-CoV-2 Using Computational Screening and Enhanced Sampling Methods

2020 ◽  
Author(s):  
Sadanandam Namsani ◽  
Debabrata Pramanik ◽  
Mohd Aamir Khan ◽  
Sudip Roy ◽  
Jayant Singh
Keyword(s):  
Free Energy ◽  
High Performance ◽  
Specific Binding ◽  
Search Space ◽  
Computational Method ◽  
Enhanced Sampling ◽  
Free Energy Surface ◽  
Drug Candidates ◽  
Computational Screening ◽  
Experimental Crystal

<p>Here, we report new chemical entities that exhibit highly specific binding to the 3-chymotrypsin-like cysteine protease (3CLpro) present in the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Because the viral 3CLpro protein controls coronavirus replication, 3CLpro is identified as a target for drug molecules. We implemented an enhanced sampling method in combination with molecular dynamics and docking to reduce the computational screening search space to four molecules that could be synthesized and tested against SARS-CoV-2. Our computational method is much more robust than any other method available for drug screening (e.g., docking) because of sampling of the free energy surface of the binding site of the protein (including the ligand) and use of explicit solvent. We have considered all possible interactions between all the atoms present in the protein, ligands, and water. Using high-performance computing with graphical processing units, we were able to perform a large number of simulations within a month and converge the results to the four most strongly bound ligands (based on free energy and other scores) from a set of 17 ligands with lower docking scores. Additionally, we have considered N3 and 13b α-ketoamide inhibitors as controls for which experimental crystal structures are available. Out of the top four ligands, PI-06 was found to have a higher screening score compared to the controls. Based on our results and analysis, we confidently claim that we have identified four potential ligands, out of which one ligand is the best choice based on free energy and the most promising candidate for further synthesis and testing against SARS-CoV-2.<br></p>

scholarly journals Exploring the free energy surface using ab initio molecular dynamics

2016 ◽  
Vol 144 (16) ◽  
pp. 164101 ◽  
Author(s):  
Amit Samanta ◽  
Miguel A. Morales ◽  
Eric Schwegler
Keyword(s):  
Molecular Dynamics ◽  
Free Energy ◽  
Ab Initio ◽  
Energy Surface ◽  
Ab Initio Molecular Dynamics ◽  
Free Energy Surface

scholarly journals Thermodynamics and free energy landscape of BAR-domain dimerization from molecular simulations

2020 ◽  
Author(s):  
Adip Jhaveri ◽  
Dhruw Maisuria ◽  
Matthew Varga ◽  
Dariush Mohammadyani ◽  
Margaret E Johnson
Keyword(s):  
Free Energy ◽  
Energy Surface ◽  
Md Simulations ◽  
Coarse Grained ◽  
Enhanced Sampling ◽  
Free Energies ◽  
Free Energy Surface ◽  
Bar Domain ◽  
Bar Domains ◽  
Protein Models

AbstractNearly all proteins interact specifically with other proteins, often forming reversible bound structures whose stability is critical to function. Proteins with BAR domains function to bind to, bend, and remodel biological membranes, where the dimerization of BAR domains is a key step in this function. Here we characterize the binding thermodynamics of homodimerization between the LSP1 BAR domain proteins in solution, using Molecular Dynamics (MD) simulations. By combining the MARTINI coarse-grained protein models with enhanced sampling through metadynamics, we construct a two-dimensional free energy surface quantifying the bound versus unbound ensembles as a function of two distance variables. Our simulations portray a heterogeneous and extraordinarily stable bound ensemble for these modeled LSP1 proteins. The proper crystal structure dimer has a large hydrophobic interface that is part of a stable minima on the free energy surface, which is enthalpically the minima of all bound structures. However, we also find several other stable nonspecific dimers with comparable free energies to the specific dimer. Through structure-based clustering of these bound structures, we find that some of these ‘nonspecific’ contacts involve extended tail regions that help stabilize the higher-order oligomers formed by BAR-domains, contacts that are separated from the homodimer interface. We find that the known membrane-binding residues of the LSP1 proteins rarely participate in any of the bound interfaces, but that both patches of residues are aligned to interact with the membrane in the specific dimer. Hence, we would expect a strong selection of the specific dimer in binding to the membrane. The effect of a 100mM NaCl buffer reduces the relative stability of nonspecific dimers compared to the specific dimer, indicating that it would help prevent aggregation of the proteins. With these results, we provide the first free energy characterization of interaction pathways in this important class of membrane sculpting domains, revealing a variety of interfacial contacts outside of the specific dimer that may help stabilize its oligomeric assemblies.

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