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Review

Applications of Molecular Dynamics Simulation in Protein Study

by
Siddharth Sinha
,
Benjamin Tam
and
San Ming Wang
*
MoE Frontiers Science Center for Precision Oncology, Cancer Center and Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macau SAR, China
*
Author to whom correspondence should be addressed.
Membranes 2022, 12(9), 844; https://doi.org/10.3390/membranes12090844
Submission received: 14 August 2022 / Revised: 24 August 2022 / Accepted: 25 August 2022 / Published: 29 August 2022
(This article belongs to the Special Issue Molecular Dynamics Simulations in Biological Membrane Systems)
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Abstract

:
Molecular Dynamics (MD) Simulations is increasingly used as a powerful tool to study protein structure-related questions. Starting from the early simulation study on the photoisomerization in rhodopsin in 1976, MD Simulations has been used to study protein function, protein stability, protein–protein interaction, enzymatic reactions and drug–protein interactions, and membrane proteins. In this review, we provide a brief review for the history of MD Simulations application and the current status of MD Simulations applications in protein studies.

1. Introduction

The essence of Molecular Simulations (MS) is a statistical mechanics and numerical method governed by the Newtonian laws of motion [1] for molecular properties, i.e., velocity, position, and energy, towards insights of molecular system while retaining macro-system physio-chemical properties. Two factors have promoted the increased application of molecular simulations over the years (Figure 1). One is the growing availability of experimentally determined protein structures, such as membrane proteins (ion channels, neurotransmitters and GPCRs etc.) [2,3], the other is the wide availability of graphics processing units (GPUs), which allows running simulations locally. MS typically analyses protein structure at a minimum of nano to micro-second time scale to reveal the dynamic nature of protein molecules covering a wide variety of biomolecular processes, such as conformational change, ligand binding and protein folding. Among the numerous approaches to MS, the Monte Carlo (MC) Simulation sampling method and the MD Simulation method are the two common methods. The basic concept of MCS is to generate an ensemble of conformation under specific thermodynamics conditions through stochastic approach; whereas the concept of MD Simulation is to iterate a time-dependent Newtonian equation of motions for hard sphere particles in a system [4,5], which can provide an ensemble of thermodynamic properties.

2. A Brief History of Molecular Simulations

MS was first introduced in 1949 by Metropolis et al. to study particle interaction [6]. Metropolis proposed a probabilistic approach to approximate the “properties” of a set of particles [6]. Instead of treating particles as individuals, simulation was applied to measure the interactions of all particles until they reach equilibrium by the governing laws. Its success inspired the development of MS by Alder and Wainwright in 1959 [7]. The early MS algorithm used a rudimentary electronic computer to iterate atom collision. Each atom was assigned an initial velocity and position. Based on the elastic collision, the MS algorithm was applied to simulate attraction and repulsion of particles. In 1964, Rahman et al. published the first study in using MS to analyze liquid Argon [8]. The work demonstrated that MS was indeed possible to analyze Lennard Jones potential for interactions between Argon atoms. In 1971, Rahman and Stilinger reported their MS study on modelling liquid water, a system composed of molecules not just atoms [9]. Their work demonstrated that differing from its solid phases structure, liquid water consists of a random network of hydrogen bonds. In 1976, Warshel and Levitt expanded MS by integrating quantum mechanics and molecular mechanics (QM/MM) to study lysozyme reaction by proposing the exchange of the classical charge of atom i and j with quantum mechanics calculations [10]. In 1977, Karplus and collaborators first used MS to study protein by using constraint method to freeze out fast-degree freedom to reach longer simulation time [11,12]. Their study led to the Noble Prize in Chemistry awarded to Warshel, Levitt and Karplus in 2013 for the development of multiscale models for complex chemical systems [10]. Anderson et al. in 1980 used MS to sample the isoenthalpic (constant pressure) ensemble. Anderson’s solution to achieve constant pressure in MD Simulation sampling was to extend dynamic variable by including volume [13]. Parrinello and Rahman showed that the scheme can be generalized to include shape and volume fluctuations by using Lagrangian mechanics. This made it possible to study the issues such as crystallization and solid–solid phase transition [14]. Their idea of extending the system dynamic variables was to assume that the system exchanges energy with a fictitious pressure or temperature reservoir. Their method took into consideration the dielectric effect caused by the atomic polarizability and increased the accuracy of the binding site. In 1985, Car and Parrinello pioneered a scheme of combining MS with direct calculation of electronic structure by means of Density Function Theory (DFT). This work was important as it indicated the possibility of combining finite temperature into simulation for electronic structure calculations, which was not possible before [15]. During 1980s and 1990s, MS approach witnessed a rise in studies of condensed matter with growing access of enhanced computing power; further leading to the challenges of phase equilibria. Moreover, to address these challenges Panagiotopolus revised the MC algorithm, known as Gibbs ensemble Monte Carlo, to distinguish the phase equilibria approach that only require to simulate the involved phases but by-pass the interface [16]. Novel algorithms such as blue moon ensemble [17] hyper-MD [18] as well as advanced theoretical methods such as Nudged-Elastic Band [19] and String [20] were devised to address the challenges of time-scales (long-time dynamics of protein folding) and rare events. Further, the advancement in quantum programs outside chemistry field and the Noble prize in Chemistry 1998 being divided equally between Walter Kohn “for his development of density-function theory” and John A. Pople “for his development of computational methods in quantum chemistry” led to form a unified approach for molecular dynamics and density-function theory. Over the following years, time-dependent density-function theory (TDDFT) further enhanced the accuracy of large-scale simulations of excited state dynamics [21,22,23]. TDDFT-MD coupled simulations to simulate excited state dynamics of biomolecules and other nanostructures achieves high accuracy through utilizing small number of basic function thereby significantly reduced the memory requirements and computation time compared to plane-wave and real-space grid bases [24]. Furthermore, utilizing multiple computer processors in parallel for MD force calculations substantially enhanced with IBM’s Blue Matter code for its Blue Gene/L general-purpose supercomputer [25], resulting in improved parallel performances for the widely used MD platforms NAMD [26] GROMACS [27] AMBER [28]. Increasing innovation and with advent of GPU (Graphics processing units) and special-purpose processors such as Anton (parallel supercomputer to enable fast MD simulations) having computing power to perform up to 20 μs/day [29] further accelerated the simulation study in different biochemical processes. However, long-timescale simulations requires stringent force field (discussed in following section) compared with short-timescale simulations. To conclude this brief history of MS, it would be appropriate to remark that MS has clearly established itself as a key scientific instrument driven by enhanced computing power, fast and efficient algorithms and force fields (FF) are demonstrated by growing number of publications utilizing both experiments and simulation tools. Major breakthroughs over the years in MS studies are shown in Figure 2.

3. Basic Concept of Force Field

Currently, it is a routine to simulate proteins with hundreds of amino acid residues at 10–100 ns surrounded by water and salt [30,31,32]. User-friendly platforms are widely available, i.e., GROMACS [33], AMBER [28], vCHARMM [34], DL_POLY [35], NAMD [26], LAMMPS [36] have been developed for MD Simulations analysis. The output of the platforms can be visualized and analyzed by external software, i.e., VMD [37], Chimera [38]. However, robust simulation requires appropriate parameters for studying a physical system. Force field, a set of mathematical expressions and parameters to describe the inter- and intra- molecular forces, are also essential to describe a physical system.
Three major molecular models have been developed: all-atom [39,40], coarse grained (CG) [41,42] and all-atom/coarse-grain mixed models [43,44,45] (Table 1). The all-atom force field for MD Simulation of lipid bilayers includes CHARMM, AMBER and OPLS-AA. GROMOS is an atomistic force field with an exception such as CHn modelled as united-atoms [46]. CHARMM (Chemistry at HARvard Macromolecular Mechanics) forcefield for lipids is widely used for simulating lipid bilayer and membrane proteins [47,48]. CHARMM force field is continuously updating and improving with the most recent version of CHARMM36m [49]. CHARMM36 lipid forcefield is parameterized for lipids [39], CHARMM36 DNA and CHARMM36 RNA are parameterized for DNA and RNA [50,51], CHARMM36m is parameterized for protein, and CHARMM General Force Field (CGenFF) is parameterized for drugs and general usage [52]. AMBER (Assisted Model Building with Energy Refinement) forcefield was developed in parallel. It treats all hydrogen atoms explicitly as CHARMM [53]. AMBER was designed and parameterized for specific biological systems: AMBER lipids 21 was parameterized for lipids [54]; AMBERff19SB was parameterized for proteins [55]; AMBER OL15 and AMBER OL3 were parameterized for DNA and RNA [56,57]; General AMBER forcefield (GAFF) was parameterized for drugs and general usage [58]; OPLS-AA (Optimized Parameters for Liquid Simulations All Atom) [59] was initially designed for simulating thermo-dynamical properties of short-chain hydrocarbons alkanes and later expanded to include lipids through a parameter set called OPLS/L [60], although the availability of lipids in the OPLS/L forcefield has not been as diverse as that of CHARMM and AMBER-compatible force fields. The latest improvement of OPLS-AA/M was its modification for peptides and protein torsional energetics [61]. The GROningen Molecular Simulation (GROMOS) forcefield utilizes a different approach for simulating analysis by fitting the parameters against experimental thermo-dynamic data. Its forcefield was generalized into a single package. The latest version is GROMOS 54A8 package updated in 2012 [62].
Compared to all-atom models, coarse-grained models significantly reduce the computing time by decreasing the number of particles explicitly during simulations. Over the last decade, coarse-grained model has also been widely used in protein [63] and nucleic acid studies [64,65]. Different coarse-grained models have been developed to extend the timescale of the simulation, since the first model used the concept of coarse grain in 1975 by Levitt and Warshal [66]. One of the most popular models is the MARTINI for membrane proteins [42], in which several atoms in protein and lipid are approximated as a single bead and four water molecules are treated as a single particle (known as one bead 4:1 mapping) although the beads can differ by their polarity or hydrophilicity. For particular cases, smaller beads can also be used, such as 3:1 and 2:1 mapping [67]. In MARTINI version 2.2, beads classified into 18 types are categorized into four groups: Q (charged), P (polar), N (intermediate) and C (apolar). In the latest version MARTINI 3, 29 beads have been sorted into seven groups with additional groups of halo-compounds (X), divalent ions (D) and water (W) [68]. MARTINI ELNEDIN model modified by utilizing an elastic network, with the peptide backbone beads position on the Cα atoms and heavier bead mass, improves the conformation transition in simulation [68]. MARTINI-Dry version provides an implicated solvation model [69]. The Born model is another model where the effects of the solvent and membrane are included implicitly in the simulation [70,71]. Implicit solvent forcefield is less used as it can cause significant errors due to it smoothen energy landscapes, which causes protein structure to deviate from the experimental crystal structure [72,73]. Coarse-grained protein models have been mainly used for analyzing protein folding mechanism and protein structure prediction [74,75]. Every alternate year, the CASP (Critical Assessment of Protein Structure Prediction) experiments provide an excellent platform to test the performance of coarse-grained models for predicting structures [76]. Several coarse-grained protein models apart from MARTINI are as follows: UNRES (united residue) [77], AWSEM (associated memory, water mediated, structure and energy model) [78], OPEP (optimized potential for efficient protein structure prediction) [79], SURPASS (Single United Residue per Pre-Averaged Secondary Structure fragment) [80] and CABS (C-alpha, c-beta, side chain) [81] models have been increasingly utilized for protein folding, structure prediction and interactions. PRIMO [82] and Scorpion [83] (solvated coarse-grained protein interaction) models are increasingly used in peptide and small protein structure prediction and protein–protein solvated complexes. The Rosetta centroid mode (CEN) model developed by Rohl et al. is also one of the widely used coarse-grained protein models in CASP protein structure prediction, de novo blind predictions, protein–protein and protein–ligand docking and modelling of protein-DNA interaction [84]. Coarse-grained models have been further utilized in nucleic acid molecular dynamics to analyze the three dimensional (3D) structural models of RNA [85,86,87]. Ding et al. introduced the discrete molecular dynamics (DMD) utilizing coarse-grained model to rapidly explore the conformational folding of RNA molecules [88]. Recently, Jonikas et al. have developed a fully automated coarse-grained model NAST (the nucleic acid simulation tool) using statistical potential capable enough to ensemble over 10,000 RNA plausible (3D) structures [89].

4. Molecular Simulations in Protein Study

The importance of MS arises from the fact that biomolecules such as proteins are under a dynamic state of motion, which is essential for the function of biomolecules. Although multiple experimental techniques can reveal the structural features of biomolecules, they are often incapable to show the dynamic features. MS provides a means to model the flexibility and conformational changes in the biomolecule at atomistic level, which is difficult to achieve by experimental approaches [11]. MS is more effective when combined with experiments to validate and improve the accuracy of experimental results. A key feature of MS is its ability to mimic both the in vitro and in vivo conditions, for example, at different pH conditions, in the presence of water and ions, at different salt or ionic concentrations, and in the presence of a lipid bilayer and other cellular components [92]. MS has been used to study multiple protein-related issues, such as protein-binding, protein–protein interaction and signaling [93]. The followings are examples.

4.1. Applications of Molecular Simulations in Membrane Proteins

MS has been increasingly applied in membrane protein analysis [94], such as membrane protein structure and organization, membrane protein permeability, lipid-protein interaction, protein–ligand interaction, protein structure and dynamics [95,96]. MS is also used in combination with a wide variety of experimental techniques to address protein structure-related questions, including X-ray crystallography, cryo-electron microscopy (cryo-EM), nuclear magnetic resonance (NMR), electron paramagnetic resonance (EPR) and Foster resonance energy transfer (FRET) [97]. For example, MS can minimize the gap between NMR structures and X-ray crystallography structures, allowing for better analysis of structural instability and interaction [98].
Membrane protein can be classified into three classes: integral, peripheral and lipid-anchored [99]. Based on the interaction of membrane protein with lipid bilayer, the three classes can be further divided into eight types: (1) type I membrane protein; (2) type II membrane protein; (3) type III membrane protein; (4) type IV membrane protein; (5) multipass transmembrane protein; (6) lipid chain-anchored membrane protein; (7) Glycosylphosphatidylinositol (GPI)-anchored membrane protein; and (8) peripheral membrane protein [99]. In a biological membrane, lipid molecules are arranged spontaneously to form a lipid bilayer having hydrophobic chains in the interior and hydrophilic groups at the exterior [100]. Membrane protein such as transporters, ion channels etc. plays significant roles in transportation of ions, polypeptides and other substrates through lipid bilayers [101]. Membrane receptor proteins responsible for signal transduction is also one of the important functions of membrane protein [102]. Compared with soluble proteins, determination of the structure for membrane proteins using X-ray, NMR and cryo-EM is more challenging, and the number of membrane protein structures in protein databases, i.e., PDB, JenaLib, OPM [103,104,105] is also limited [106,107]. Furthermore, as membrane proteins often undergo large conformational changes, a single structure is not sufficient to understand the mechanism of their biological function. Therefore, increasing attention has been paid in applying simulations to study membrane proteins. The structures of many membrane proteins have been experimentally determined, e.g., many ion channels, neurotransmitters, transporters and G protein-coupled receptors (GPCRs) etc., the information facilitate simulation study. Furthermore, the increased power and accessibility of MD Simulation by computer hardware, particularly GPU (graphical processing unit), allows simulations to be run locally at modest cost [108,109,110]. Nowadays, simulation is often applied in the timescale of microseconds, thus making it possible to trace biological events from the early studies, which primarily focused on phospholipid bilayers such as DPPC (dipalmitoylphosphatidylcholine) or DMPC (dimyristoylphosphatidylcholine) [40,111,112]. To simulate various biological phenomena such as aggregation, large conformational changes and membrane protein folding, Hensman and Okamoto first applied the enhanced conformational sampling method [113,114,115,116,117]. They compared the accuracy and efficiency of different molecular models in glycoprotein A (GpA), phospholamban (PLN), amyloid precursor protein (APP) and mixed lipid bilayers [118], and observed that the predicted GpA, PLN and APP structures using the replica-exchange MD (REMD) and replica-exchange umbrella sampling (REUS) approaches are comparable with the data from experiments, suggesting that the model and simulation approaches are sufficiently accurate.

4.2. Simulations of Integral Membrane Protein (GPCRs)

G protein-coupled receptors (GPCRs) are internal membrane proteins (IMPs) consisting of 7-transmembrane helix. They are the largest membrane receptors. There are about 800 GPCRs identified in the human genome [119], over a quarter of drugs target GPCRs [120,121,122]. In 2020, 24 new drugs targeting 16 GPCRs have been clinically approved, and 44 new drugs targeting GPCRs were under 100 clinical trials [123]. Simulation studies have drastically helped improve understanding of GPCRs structures and functions [124,125]. Dahl and Weinstein (1990) pioneered the MD Simulations studies of GPCR on dopamine, serotonin and opioid receptors [126]. With the X-ray determined crystal β2AR structures [127], microsecond-long MD Simulation of β2AR reveal multiple cholesterol (lipid bilayer) interactions distributed unequally between the extracellular (EC) and intracellular (IC) sides with variable binding strength [128]. There are three key areas where MD Simulations provide unique insights into dynamic properties of GPCRs: the change in conformations that occur between different GPCR active and inactive states, interaction of GPCRs with ligand/inhibitors and effects of lipids on the conformational dynamics of GPCRs.
Dror and colleagues utilized long time-scale MD Simulations to identify key connector region that connects GPCR canonical binding sites to G-protein binding site [129], Moreover, the conformations of the G-protein were key determinant as the inactive G-protein binding site restricts the connector region (GPCRs) to its inactive conformation [129]. The study performed a total of 92 simulations for ~656 µs time period to analyze the mechanism for GPCRs transition from inactive to active state. Further, using similar protocol and Anton (a supercomputer designed for accelerating MD Simulations) [130], Schneider and colleagues performed MD Simulations to analyze the differences between full agonists (Morphine) and biased agonists (TRV-130) in mutual information networks for the µ opioid receptor active state (PDB: 5C1M) [131]. The results clearly indicated that biased inhibitors interact with smaller set of residues, thereby make it easy to analyze the binding pattern experimentally.
GPCRs represent a broad spectrum of drug targets as they have pivotal roles in many physiological functions (neurotransmitters, environmental stimulus, chemokines etc.) and in disease development including cancer and cancer metastasis [132]. GPCRs are particularly useful for drug discovery due to their ability to modulate a variety of intracellular signaling pathways, including the activation of G proteins and β-arrestins [133]. Identification of novel molecules targeting GPCRs face several challenges as these proteins exist in different conformations rather than a single inactive and activated state [125]. Recent studies have used long unbiased MD Simulations for ~ 50 µs to predict the binding poses of TRV-130 to the μ-opioid receptor (MOR) [131], the allosteric ligands to the M2 muscarinic receptor (M2) [134], and ML056 to the sphingosine-1-phosphate receptor 1 (S1P1R) [135]. Further, Marino et al. applied meta-dynamics to study the ligand binding to GPCRs to predict the binding pose of a PAM, BMS-986187 to the δ-opioid receptor (DOR) as well as to MOR (G protein agonist) [136]. Further, the need to develop new protocols to decrease the computational time and increase the performance of the algorithms, resulted in Supervised MD (SuMD) capable of reducing the total simulation time from microsecond to nanosecond timescale [137]. The SuMD protocol was applied to binding analysis of numerous ligands to the A2A adenosine receptor, resulting in significantly reducing the simulation time, for example the analysis of ZM241385 (PDB: 3EML), T4G (PDB: 3UZA), T4E (PDB: 3UZC) reproduced the crystallographic pose in approx. 60ns, 65ns and 110ns, respectively [137]. MD Simulations can reveal specific GPCR residues and ligand–receptor interactions responsible for the allosteric transmission, based on dynamical information derived from the simulations.
Lipids (cholesterol, etc.) also play a role in the function of GPCRs in addition to ligands and ions [138,139]. Early studies utilizing classical MD Simulation of A2A adenosine-bound receptor (PDB: 2YDO) resulted in identification of potential cholesterol sites in GPCRs [140] with three binding sites. The third binding site, especially, demonstrated the same binding pattern in alignment with X-ray crystallographic structure of same receptor (PDB: 4EIY) [141]. MD Simulations was also utilized to analyze the mechanism of other lipids (simple/mixed zwitterionic bilayers) modulating A2A receptor structure [142]. Simulations for 0.25 ms revealed that the lipid bilayers had different effects on the stability of the active state of native receptor. Moreover, simulation studies revealed that phospholipids can compete with G-protein binding site, suggesting that lipid binding at intracellular end can hinder G-protein binding, leading to modulation of GPCRs by phospholipids [143]. Further, a GPCR database has been developed, with reference data and tools for both analysis and visualization [144,145].

4.3. Simulations of Interaction between SARS-CoV-2 Spike and Membrane ACE2 Receptor

The outbreak of COVID-19 caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is an example of showing how MD Simulations can be used to understand the relationship between SARS-CoV-2 and the human host. SARS-CoV-2 infects human cells through its spike (S) protein binding to the angiotensin-converting enzyme-2 (ACE-2) receptor in the human cell membrane. SARS-CoV-2 constantly mutated its spike to increase its infection to the host. New mutants including alpha, beta, gamma, delta and omicron strains have been generated [146] carrying L452R, T478K, E484K, E484Q, and N501Y mutations. A typical example is the delta mutant, which contains 10 mutations of T19R, G142D, 156del, 157del, R158G, L452R, T478K, D614G, P681R, D950N in its S protein, and the double mutation L452R/T478K is located in RBD [147] (Figure 3).
MD Simulations provides a powerful tool to reveal the structural and conformational basis of the new mutants to ACE2 [148]. Massive-scale MD Simulations using state of art supercomputer machines have been used to gain insights into the biology of SARS-CoV-2 [149]. Amaro et al. used ~250,000 processing cores and ~4000 processor nodes in their MD Simulations study [150]; their results showed that glycans play a significant role in S-protein binding [151]. Taiji et al. used a drug discovery supercomputer MD GRAPE-4A to analyze the structural dynamics of Mpro of SARS-CoV-2 [152]. Acharya et al. used a supercomputer “Summit” to perform MD Simulations on 8000 compounds to screen for potent inhibitors to S-protein and identified 77 small-molecule drug compounds [153]. Remarkably, the folding@home computing project involving over a million-citizen scientists performed an unprecedented 0.1 second MD Simulations to simulate SARS-CoV-2 [154], revealed how the S-protein uses conformational change to escape host immunity, and subsequently identified the hidden cryptic pockets that were extremely difficult to capture by experimental approaches.
We also applied MD Simulations to study the effects of SARS-CoV-2 mutations on RBD domain binding affinity with ACE2. We studied the mechanism of the increased transmissibility of SARS-CoV-2 variants with double RBD mutations [149] by investigating the changes in binding pattern and structural conformation between the ACE2 receptor and four SARS-CoV-2 variants containing three RBD double mutations of L452R/T478K (delta) [155], L452R/E484Q (kappa) [156] and E484K/N501Y (beta, gamma) [157,158]. We used a combinational approach in the study, including 3D-protein structure, protein–protein interaction, molecular dynamics simulation, superimposed protein structure, affinity binding, and antibody binding mapping. We observed that the N501Y caused mild structural change and increased the binding affinity of the S protein to ACE2 [159]. We also observed that the binding energy of N501Y variants increased to –48.92 kcal mol−1, consistent with the observations by other in vitro studies showing the binding of Y501 increased 10-fold gain of binding affinity and in vivo studies showing N501Y imparted cross-species transmission [160,161,162]. E484 has a positive (opposed) binding affinity with the ACE2, but the variant K484 has significantly increased its binding affinity with the ACE2 [163]. This indirectly changed RBD structure configuration and strengthen other key binding residues (i.e., Y505, F486) in the RBM during the spike protein approaching the ACE2, leading to the increased binding affinity [164,165]. The substitution of K, Q, or P residues at the E484 position was identified and these variants assisted the virus to escape host immune defenses [166]. E484K mutation caused a 50% loss of neutralizing activities by antibodies, and a 3 to 6-fold reduction in neutralization by sera of the individuals who received mRNA-vaccine. Simulation with 26 common antibodies found in humans showed that up to 85% showed weaker binding affinities to the E484K mutated strain [167]. Double-mutation in the beta and gamma strains increased the binding strength of RBD as they changed the energy landscape of the RBD by ~25%. The combination of E484K immune escape capabilities and N501Y increased the binding affinity, causing ~50% higher transmissibility [168]. Our study showed that the three double mutated RBD all alter the wildtype RBD structure in the ways much different from those caused by the RBD single mutations, enhanced the binding of the mutated RBD to ACE2 receptor, changed antibody binding, leading to the increased infection of SARS-CoV-2 to the host cells (Figure 4).

5. Challenges and Future Opportunities

Increments and leaps of improvements are continuously produced by many research groups and developing new solutions for various persistent challenges remain the focus of research. At present, managing enormous information generated by simulations with every molecule represented in atomistic detail is a big challenge. Currently, it is impractical to share the primary data as there is no MD Simulations database [169,170,171,172]. Possible solutions include reducing the data size by using snapshots at different time points of simulation and removing insignificant parts of the system such as solvent, and maintaining the full dataset but allowing remote analysis so that only the results instead of the actual dataset need to be transmitted [173].
The connection between experiments and simulations is an important step complementary to each other [173,174]. This can be further enhanced through improving the FF or the method of simulations. For example, simulation accuracy can be significantly improved by integrating quantum mechanics on-the-fly simulation. However, many theoretical challenges and long computational time may prohibit the merging of quantum mechanics with molecular mechanics. The progress in this area remains stagnated since the 2000s mainly due to the number of electrons (i.e., number of basis sets to represent electronic wave function) involved in a system, integration of time, and calculation of the system beyond ground state [175]. To circumvent such problem, polarizable FF were developed to approximate dielectric effects in MD Simulations. However, there is a need to develop a polarizable FF for better accuracy than current versions of FF (AMBER99SB-ILDN, CHARMM22-CMA, GROMOS, OPLS-AA) in order to study protein in multi-scale environments [174].
Another key area of research gaining momentum is the integration of machine learning (ML) and deep learning (DL) techniques into MD Simulations. The incorporation brought various significant new research directions to analyze protein trajectories and protein structures. ML and DL can analyze non-linear complex systems by recognizing regular and similar patterns in the data. In particular, substantial expansion has been made that ML and DL utilize to create an adaptive force field on-the-fly [176,177,178], increasing the simulation timescale [179], and protein–protein/protein–ligand interactions [180,181]. ML and DL are becoming new potential tools for analyzing large amounts of data produced by MD Simulations.
Currently, it remains a challenge for researchers without high-end computing backgrounds to use MS to study the system of their interest. A user-friendly interface such as automation in MD Simulations need to develop. One such remarkable example was made by P. Arantes et al. (2021). They presented a user-friendly front-end running MD Simulations system using openMM toolkit on the Google colab framework [182] and cloud-computing scheme for performing MD Simualtions on microsecond time scale. Regardless the challenges, MD analysis is becoming a mainstream tool in basic and applied biology.

Author Contributions

S.S., B.T.: manuscript preparation, figures, revisions, literature review, visualization. S.M.W.: manuscript review & editing, supervision, funding acquisition. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by grants from the Macau Science and Technology Development Fund (085/2017/A2, 0077/2019/AMJ), the University of Macau (SRG2017-00097-FHS, MYRG2019-00018-FHS, MYRG2020-00094-FHS), the Faculty of Health Sciences, University of Macau (FHSIG/SW/0007/2020P and a startup fund) to SMW. BT is the recipient of University of Macau Postdoctoral Fellowship Class A of the Macao Talent Program and FDCT post-doctorate fellowship (UMMTP-FDCT/0027/APD/2021).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

We are thankful for the Information and Communication Technology Office (ICTO), the University of Macau for providing the High-Performance Computing Cluster (HPCC) resource and facilities for the study.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Karplus, M.; McCammon, J.A. Molecular dynamics simulations of biomolecules. Nat. Struct. Mol. Biol. 2002, 9, 646–652. [Google Scholar] [CrossRef] [PubMed]
  2. Minor, D.L., Jr. The neurobiologist’s guide to structural biology: A primer on why macromolecular structure matters and how to evaluate structural data. Neuron 2007, 54, 511–533. [Google Scholar] [CrossRef] [PubMed]
  3. Coleman, J.A.; Green, E.M.; Gouaux, E. X-ray structures and mechanism of the human serotonin transporter. Nature 2016, 532, 334–339. [Google Scholar] [CrossRef] [PubMed]
  4. Metropolis, N.; Rosenbluth, A.W.; Rosenbluth, M.N.; Teller, A.H.; Teller, E. Equation of State Calculations by Fast Computing Machines. J. Chem. Phys. 1953, 21, 6. [Google Scholar] [CrossRef]
  5. Alder, B.J.; Wainwright, T.E. Phase Transition for a Hard Sphere System. J. Chem. Phys. 1957, 27, 1208–1209. [Google Scholar] [CrossRef]
  6. Metropolis, N.; Ulam, S. The Monte Carlo Method. J. Am. Stat. Assoc. 1949, 44, 335–341. [Google Scholar] [CrossRef] [PubMed]
  7. Alder, B.J.; Wainwright, T.E. Studies in Molecular Dynamics. I. General Method. J. Chem. Phys. 1959, 31, 459–466. [Google Scholar] [CrossRef]
  8. Rahman, A. Correlations in the Motion of Atoms in Liquid Argon. Phys. Rev. 1964, 136, A405–A411. [Google Scholar] [CrossRef]
  9. Rahman, A.; Stillinger, F.H. Molecular Dynamics Study of Liquid Water. J. Chem. Phys. 1971, 55, 3336–3359. [Google Scholar] [CrossRef]
  10. Warshel, A.; Levitt, M. Theoretical studies of enzymic reactions: Dielectric, electrostatic and steric stabilization of the carbonium ion in the reaction of lysozyme. J. Mol. Biol. 1976, 103, 227–249. [Google Scholar] [CrossRef]
  11. McCammon, J.A.; Gelin, B.R.; Karplus, M. Dynamics of folded proteins. Nature 1977, 267, 585–590. [Google Scholar] [CrossRef] [PubMed]
  12. Ryckaert, J.-P.; Ciccotti, G.; Berendsen, H.J.C. Numerical integration of the cartesian equations of motion of a system with constraints: Molecular dynamics of n-alkanes. J. Comput. Phys. 1977, 23, 327–341. [Google Scholar] [CrossRef]
  13. Andersen, H.C. Molecular dynamics simulations at constant pressure and/or temperature. J. Chem. Phys. 1980, 72, 2384–2393. [Google Scholar] [CrossRef]
  14. Parrinello, M.; Rahman, A. Polymorphic transitions in single crystals: A new molecular dynamics method. J. Appl. Phys. 1981, 52, 7182–7190. [Google Scholar] [CrossRef]
  15. Car, R.; Parrinello, M. Unified approach for molecular dynamics and density-functional theory. Phys. Rev. Lett. 1985, 55, 2471–2474. [Google Scholar] [CrossRef] [PubMed]
  16. Panagiotopoulos, A.Z. Direct determination of phase coexistence properties of fluids by Monte Carlo simulation in a new ensemble. Mol. Phys. 1987, 61, 813–826. [Google Scholar] [CrossRef]
  17. Carter, E.A.; Ciccotti, G.; Hynes, J.T.; Kapral, R. Constrained reaction coordinate dynamics for the simulation of rare events. Chem. Phys. Lett. 1989, 156, 472–477. [Google Scholar] [CrossRef]
  18. Voter, A.F. A method for accelerating the molecular dynamics simulation of infrequent events. J. Chem. Phys. 1997, 106, 4665–4677. [Google Scholar] [CrossRef]
  19. Mills, G.; Jacobsen, W. Classical and Quantum Dynamics in Condensed Phase Simulations; World Scientific: Singapore, 1998. [Google Scholar]
  20. Weinan, E.; Ren, W.; Vanden-Eijnden, E. String method for the study of rare events. Phys. Rev. B 2002, 66, 052301. [Google Scholar]
  21. Ben-Nun, M.; Quenneville, J.; Martínez, T.J. Ab Initio Multiple Spawning:  Photochemistry from First Principles Quantum Molecular Dynamics. J. Phys. Chem. A 2000, 104, 5161–5175. [Google Scholar] [CrossRef]
  22. Jones, C.M.; List, N.H.; Martínez, T.J. Steric and Electronic Origins of Fluorescence in GFP and GFP-like Proteins. J. Am. Chem. Soc. 2022, 144, 12732–12746. [Google Scholar] [CrossRef] [PubMed]
  23. Wasif Baig, M.; Pederzoli, M.; Kývala, M.; Cwiklik, L.; Pittner, J. Theoretical Investigation of the Effect of Alkylation and Bromination on Intersystem Crossing in BODIPY-Based Photosensitizers. J. Phys. Chem. B 2021, 125, 11617–11627. [Google Scholar] [CrossRef] [PubMed]
  24. Meng, S.; Kaxiras, E. Real-time, local basis-set implementation of time-dependent density functional theory for excited state dynamics simulations. J. Chem. Phys. 2008, 129, 054110. [Google Scholar] [CrossRef]
  25. Fitch, B.G.; Rayshubskiy, A.; Eleftheriou, M.; Ward, T.J.C.; Giampapa, M.; Zhestkov, Y.; Pitman, M.C.; Suits, F.; Grossfield, A.; Pitera, J.; et al. Blue Matter: Strong Scaling of Molecular Dynamics on Blue Gene/L. Comp. Sci. 2006, 3992, 846–854. [Google Scholar]
  26. Phillips, J.C.; Braun, R.; Wang, W.; Gumbart, J.; Tajkhorshid, E.; Villa, E.; Chipot, C.; Skeel, R.D.; Kalé, L.; Schulten, K. Scalable molecular dynamics with NAMD. J. Comput. Chem. 2005, 26, 1781–1802. [Google Scholar] [CrossRef] [PubMed]
  27. Hess, B.; Kutzner, C.; van der Spoel, D.; Lindahl, E. GROMACS 4:  Algorithms for Highly Efficient, Load-Balanced, and Scalable Molecular Simulation. J. Chem. Theory Comput. 2008, 4, 435–447. [Google Scholar] [CrossRef]
  28. Case, D.A.; Cheatham, T.E., 3rd; Darden, T.; Gohlke, H.; Luo, R.; Merz, K.M., Jr.; Onufriev, A.; Simmerling, C.; Wang, B.; Woods, R.J. The Amber biomolecular simulation programs. J. Comput. Chem. 2005, 26, 1668–1688. [Google Scholar] [CrossRef]
  29. Shaw, D.E.; Dror, R.O.; Salmon, J.K.; Grossman, J.P.; Mackenzie, K.M.; Bank, J.A.; Young, C.; Deneroff, M.M.; Batson, B.; Bowers, K.J.; et al. Millisecond-scale molecular dynamics simulations on Anton. In Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis, Portland, OR, USA, 14–20 November 2009; p. 65. [Google Scholar]
  30. Levitt, M.; Sharon, R. Accurate simulation of protein dynamics in solution. Proc. Natl. Acad. Sci. USA 1988, 85, 7557–7561. [Google Scholar] [CrossRef]
  31. Mackerell, A.D., Jr. Empirical force fields for biological macromolecules: Overview and issues. J. Comput. Chem. 2004, 25, 1584–1604. [Google Scholar] [CrossRef] [PubMed]
  32. Price, D.J.; Brooks, C.L., 3rd. Modern protein force fields behave comparably in molecular dynamics simulations. J. Comput. Chem. 2002, 23, 1045–1057. [Google Scholar] [CrossRef]
  33. Van Der Spoel, D.; Lindahl, E.; Hess, B.; Groenhof, G.; Mark, A.E.; Berendsen, H.J. GROMACS: Fast, flexible, and free. J. Comput. Chem. 2005, 26, 1701–1718. [Google Scholar] [CrossRef] [PubMed]
  34. Brooks, B.R.; Brooks, C.L., 3rd; Mackerell, A.D., Jr.; Nilsson, L.; Petrella, R.J.; Roux, B.; Won, Y.; Archontis, G.; Bartels, C.; Boresch, S.; et al. CHARMM: The biomolecular simulation program. J. Comput. Chem. 2009, 30, 1545–1614. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  35. Smith, W.; Yong, C.W.; Rodger, P.M. DL_POLY: Application to molecular simulation. Mol. Simul. 2002, 28, 385–471. [Google Scholar] [CrossRef]
  36. Plimpton, S. Fast Parallel Algorithms for Short-Range Molecular Dynamics. J. Comput. Phys. 1995, 117, 1–19. [Google Scholar] [CrossRef]
  37. Humphrey, W.; Dalke, A.; Schulten, K. VMD: Visual molecular dynamics. J. Mol. Graph. 1996, 14, 33–38. [Google Scholar] [CrossRef]
  38. Pettersen, E.F.; Goddard, T.D.; Huang, C.C.; Couch, G.S.; Greenblatt, D.M.; Meng, E.C.; Ferrin, T.E. UCSF Chimera—A visualization system for exploratory research and analysis. J. Comput. Chem. 2004, 25, 1605–1612. [Google Scholar] [CrossRef]
  39. Klauda, J.B.; Venable, R.M.; Freites, J.A.; O’Connor, J.W.; Tobias, D.J.; Mondragon-Ramirez, C.; Vorobyov, I.; MacKerell, A.D., Jr.; Pastor, R.W. Update of the CHARMM all-atom additive force field for lipids: Validation on six lipid types. J. Phys. Chem. B 2010, 114, 7830–7843. [Google Scholar] [CrossRef]
  40. Moore, P.B.; Lopez, C.F.; Klein, M.L. Dynamical properties of a hydrated lipid bilayer from a multinanosecond molecular dynamics simulation. Biophys. J. 2001, 81, 2484–2494. [Google Scholar] [CrossRef]
  41. Saiz, L.; Klein, M.L. Computer simulation studies of model biological membranes. Acc. Chem. Res. 2002, 35, 482–489. [Google Scholar] [CrossRef]
  42. Marrink, S.J.; Risselada, H.J.; Yefimov, S.; Tieleman, D.P.; de Vries, A.H. The MARTINI Force Field:  Coarse Grained Model for Biomolecular Simulations. J. Phys. Chem. B 2007, 111, 7812–7824. [Google Scholar] [CrossRef]
  43. Shi, Q.; Izvekov, S.; Voth, G.A. Mixed atomistic and coarse-grained molecular dynamics: Simulation of a membrane-bound ion channel. J. Phys. Chem. B 2006, 110, 15045–15048. [Google Scholar] [CrossRef] [PubMed]
  44. Wan, C.-K.; Han, W.; Wu, Y.-D. Parameterization of PACE Force Field for Membrane Environment and Simulation of Helical Peptides and Helix–Helix Association. J. Chem. Theory Comput. 2012, 8, 300–313. [Google Scholar] [CrossRef]
  45. Kar, P.; Gopal, S.M.; Cheng, Y.-M.; Panahi, A.; Feig, M. Transferring the PRIMO Coarse-Grained Force Field to the Membrane Environment: Simulations of Membrane Proteins and Helix–Helix Association. J. Chem. Theory Comput. 2014, 10, 3459–3472. [Google Scholar] [CrossRef] [PubMed]
  46. Daura, X.; Mark, A.E.; Van Gunsteren, W.F. Parametrization of aliphatic CHn united atoms of GROMOS96 force field. J. Comput. Chem. 1998, 19, 535–547. [Google Scholar] [CrossRef]
  47. Best, R.B.; Zhu, X.; Shim, J.; Lopes, P.E.M.; Mittal, J.; Feig, M.; MacKerell, A.D. Optimization of the Additive CHARMM All-Atom Protein Force Field Targeting Improved Sampling of the Backbone ϕ, ψ and Side-Chain χ1 and χ2 Dihedral Angles. J. Chem. Theory Comput. 2012, 8, 3257–3273. [Google Scholar] [CrossRef] [PubMed]
  48. MacKerell, A.D.; Bashford, D.; Bellott, M.; Dunbrack, R.L.; Evanseck, J.D.; Field, M.J.; Fischer, S.; Gao, J.; Guo, H.; Ha, S.; et al. All-Atom Empirical Potential for Molecular Modeling and Dynamics Studies of Proteins. J. Phys. Chem. B 1998, 102, 3586–3616. [Google Scholar] [CrossRef]
  49. Huang, J.; Rauscher, S.; Nawrocki, G.; Ran, T.; Feig, M.; de Groot, B.L.; Grubmüller, H.; MacKerell, A.D., Jr. CHARMM36m: An improved force field for folded and intrinsically disordered proteins. Nat. Methods 2017, 14, 71–73. [Google Scholar] [CrossRef]
  50. Hart, K.; Foloppe, N.; Baker, C.M.; Denning, E.J.; Nilsson, L.; MacKerell, A.D. Optimization of the CHARMM Additive Force Field for DNA: Improved Treatment of the BI/BII Conformational Equilibrium. J. Chem. Theory Comput. 2012, 8, 348–362. [Google Scholar] [CrossRef]
  51. Denning, E.J.; Priyakumar, U.D.; Nilsson, L.; Mackerell, A.D., Jr. Impact of 2′-hydroxyl sampling on the conformational properties of RNA: Update of the CHARMM all-atom additive force field for RNA. J. Comput. Chem. 2011, 32, 1929–1943. [Google Scholar] [CrossRef]
  52. Vanommeslaeghe, K.; Hatcher, E.; Acharya, C.; Kundu, S.; Zhong, S.; Shim, J.; Darian, E.; Guvench, O.; Lopes, P.; Vorobyov, I.; et al. CHARMM general force field: A force field for drug-like molecules compatible with the CHARMM all-atom additive biological force fields. J. Comput. Chem. 2010, 31, 671–690. [Google Scholar] [CrossRef]
  53. Yu, W.; He, X.; Vanommeslaeghe, K.; MacKerell, A.D., Jr. Extension of the CHARMM General Force Field to sulfonyl-containing compounds and its utility in biomolecular simulations. J. Comput. Chem. 2012, 33, 2451–2468. [Google Scholar] [CrossRef] [PubMed]
  54. Dickson, C.J.; Walker, R.C.; Gould, I.R. Lipid21: Complex Lipid Membrane Simulations with AMBER. J. Chem. Theory Comput. 2022, 18, 1726–1736. [Google Scholar] [CrossRef] [PubMed]
  55. Tian, C.; Kasavajhala, K.; Belfon, K.A.A.; Raguette, L.; Huang, H.; Migues, A.N.; Bickel, J.; Wang, Y.; Pincay, J.; Wu, Q.; et al. ff19SB: Amino-Acid-Specific Protein Backbone Parameters Trained against Quantum Mechanics Energy Surfaces in Solution. J. Chem. Theory Comput. 2020, 16, 528–552. [Google Scholar] [CrossRef] [PubMed]
  56. Galindo-Murillo, R.; Robertson, J.C.; Zgarbová, M.; Šponer, J.; Otyepka, M.; Jurečka, P.; Cheatham, T.E. Assessing the Current State of Amber Force Field Modifications for DNA. J. Chem. Theory Comput. 2016, 12, 4114–4127. [Google Scholar] [CrossRef]
  57. Zgarbová, M.; Otyepka, M.; Šponer, J.; Mládek, A.; Banáš, P.; Cheatham, T.E.; Jurečka, P. Refinement of the Cornell et al. Nucleic Acids Force Field Based on Reference Quantum Chemical Calculations of Glycosidic Torsion Profiles. J. Chem. Theory Comput. 2011, 7, 2886–2902. [Google Scholar] [CrossRef]
  58. Wang, J.; Wolf, R.M.; Caldwell, J.W.; Kollman, P.A.; Case, D.A. Development and testing of a general amber force field. J. Comput. Chem. 2004, 25, 1157–1174. [Google Scholar] [CrossRef]
  59. Jorgensen, W.L.; Maxwell, D.S.; Tirado-Rives, J. Development and Testing of the OPLS All-Atom Force Field on Conformational Energetics and Properties of Organic Liquids. J. Am. Chem. Soc. 1996, 118, 11225–11236. [Google Scholar] [CrossRef]
  60. Komáromi, I.; Owen, M.C.; Murphy, R.F.; Lovas, S. Development of glycyl radical parameters for the OPLS-AA/L force field. J. Comput. Chem. 2008, 29, 1999–2009. [Google Scholar] [CrossRef]
  61. Robertson, M.J.; Tirado-Rives, J.; Jorgensen, W.L. Improved Peptide and Protein Torsional Energetics with the OPLS-AA Force Field. J. Chem. Theory Comput. 2015, 11, 3499–3509. [Google Scholar] [CrossRef]
  62. Reif, M.M.; Hünenberger, P.H.; Oostenbrink, C. New Interaction Parameters for Charged Amino Acid Side Chains in the GROMOS Force Field. J. Chem. Theory Comput. 2012, 8, 3705–3723. [Google Scholar] [CrossRef]
  63. Kmiecik, S.; Gront, D.; Kolinski, M.; Wieteska, L.; Dawid, A.E.; Kolinski, A. Coarse-Grained Protein Models and Their Applications. Chem. Rev. 2016, 116, 7898–7936. [Google Scholar] [CrossRef] [PubMed]
  64. Uusitalo, J.J.; Ingólfsson, H.I.; Akhshi, P.; Tieleman, D.P.; Marrink, S.J. Martini Coarse-Grained Force Field: Extension to DNA. J. Chem. Theory Comput. 2015, 11, 3932–3945. [Google Scholar] [CrossRef] [PubMed]
  65. Šulc, P.; Romano, F.; Ouldridge, T.E.; Doye, J.P.; Louis, A.A. A nucleotide-level coarse-grained model of RNA. J. Chem. Phys. 2014, 140, 235102. [Google Scholar] [CrossRef] [PubMed]
  66. Levitt, M.; Warshel, A. Computer simulation of protein folding. Nature 1975, 253, 694–698. [Google Scholar] [CrossRef]
  67. De Jong, D.H.; Singh, G.; Bennett, W.F.D.; Arnarez, C.; Wassenaar, T.A.; Schäfer, L.V.; Periole, X.; Tieleman, D.P.; Marrink, S.J. Improved Parameters for the Martini Coarse-Grained Protein Force Field. J. Chem. Theory Comput. 2013, 9, 687–697. [Google Scholar] [CrossRef] [PubMed]
  68. Periole, X.; Cavalli, M.; Marrink, S.-J.; Ceruso, M.A. Combining an Elastic Network With a Coarse-Grained Molecular Force Field: Structure, Dynamics, and Intermolecular Recognition. J. Chem. Theory Comput. 2009, 5, 2531–2543. [Google Scholar] [CrossRef]
  69. Arnarez, C.; Uusitalo, J.J.; Masman, M.F.; Ingólfsson, H.I.; de Jong, D.H.; Melo, M.N.; Periole, X.; de Vries, A.H.; Marrink, S.J. Dry Martini, a Coarse-Grained Force Field for Lipid Membrane Simulations with Implicit Solvent. J. Chem. Theory Comput. 2015, 11, 260–275. [Google Scholar] [CrossRef]
  70. Bashford, D.; Case, D.A. Generalized born models of macromolecular solvation effects. Annu. Rev. Phys. Chem. 2000, 51, 129–152. [Google Scholar] [CrossRef]
  71. Im, W.; Chen, J.; Brooks, C.L. Peptide and Protein Folding and Conformational Equilibria: Theoretical Treatment of Electrostatics and Hydrogen Bonding with Implicit Solvent Models. In Advances in Protein Chemistry; Academic Press: Cambridge, MA, USA, 2005; Volume 72, pp. 173–198. [Google Scholar]
  72. Zhang, J.; Zhang, H.; Wu, T.; Wang, Q.; van der Spoel, D. Comparison of Implicit and Explicit Solvent Models for the Calculation of Solvation Free Energy in Organic Solvents. J. Chem. Theory Comput. 2017, 13, 1034–1043. [Google Scholar] [CrossRef]
  73. Zhou, R. Free energy landscape of protein folding in water: Explicit vs. implicit solvent. Proteins Struct. Funct. Bioinform. 2003, 53, 148–161. [Google Scholar] [CrossRef]
  74. Chouard, T. Structural biology: Breaking the protein rules. Nature 2011, 471, 151–153. [Google Scholar] [CrossRef] [PubMed]
  75. Henzler-Wildman, K.; Kern, D. Dynamic personalities of proteins. Nature 2007, 450, 964–972. [Google Scholar] [CrossRef]
  76. Moult, J.; Fidelis, K.; Kryshtafovych, A.; Schwede, T.; Tramontano, A. Critical assessment of methods of protein structure prediction (CASP)--round x. Proteins 2014, 82 (Suppl. S2), 1–6. [Google Scholar] [CrossRef] [PubMed]
  77. Liwo, A.; Baranowski, M.; Czaplewski, C.; Gołaś, E.; He, Y.; Jagieła, D.; Krupa, P.; Maciejczyk, M.; Makowski, M.; Mozolewska, M.A.; et al. A unified coarse-grained model of biological macromolecules based on mean-field multipole-multipole interactions. J. Mol. Modeling 2014, 20, 2306. [Google Scholar] [CrossRef]
  78. Davtyan, A.; Schafer, N.P.; Zheng, W.; Clementi, C.; Wolynes, P.G.; Papoian, G.A. AWSEM-MD: Protein Structure Prediction Using Coarse-Grained Physical Potentials and Bioinformatically Based Local Structure Biasing. J. Phys. Chem. B 2012, 116, 8494–8503. [Google Scholar] [CrossRef]
  79. Sterpone, F.; Melchionna, S.; Tuffery, P.; Pasquali, S.; Mousseau, N.; Cragnolini, T.; Chebaro, Y.; St-Pierre, J.-F.; Kalimeri, M.; Barducci, A.; et al. The OPEP protein model: From single molecules, amyloid formation, crowding and hydrodynamics to DNA/RNA systems. Chem. Soc. Rev. 2014, 43, 4871–4893. [Google Scholar] [CrossRef] [PubMed]
  80. Dawid, A.E.; Gront, D.; Kolinski, A. SURPASS Low-Resolution Coarse-Grained Protein Modeling. J. Chem. Theory Comput. 2017, 13, 5766–5779. [Google Scholar] [CrossRef]
  81. Kolinski, A. Protein modeling and structure prediction with a reduced representation. Acta Biochim. Pol. 2004, 51, 349–371. [Google Scholar] [CrossRef]
  82. Kar, P.; Gopal, S.M.; Cheng, Y.-M.; Predeus, A.; Feig, M. PRIMO: A Transferable Coarse-Grained Force Field for Proteins. J. Chem. Theory Comput. 2013, 9, 3769–3788. [Google Scholar] [CrossRef]
  83. Basdevant, N.; Borgis, D.; Ha-Duong, T. Modeling Protein–Protein Recognition in Solution Using the Coarse-Grained Force Field SCORPION. J. Chem. Theory Comput. 2013, 9, 803–813. [Google Scholar] [CrossRef]
  84. Rohl, C.A.; Strauss, C.E.; Misura, K.M.; Baker, D. Protein structure prediction using Rosetta. Methods Enzym. 2004, 383, 66–93. [Google Scholar] [CrossRef]
  85. Cao, S.; Chen, S.J. Predicting structures and stabilities for H-type pseudoknots with interhelix loops. RNA 2009, 15, 696–706. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  86. Setny, P.; Zacharias, M. Elastic Network Models of Nucleic Acids Flexibility. J. Chem. Theory Comput. 2013, 9, 5460–5470. [Google Scholar] [CrossRef] [PubMed]
  87. Boniecki, M.J.; Lach, G.; Dawson, W.K.; Tomala, K.; Lukasz, P.; Soltysinski, T.; Rother, K.M.; Bujnicki, J.M. SimRNA: A coarse-grained method for RNA folding simulations and 3D structure prediction. Nucleic Acids Res. 2016, 44, e63. [Google Scholar] [CrossRef] [PubMed]
  88. Ding, F.; Sharma, S.; Chalasani, P.; Demidov, V.V.; Broude, N.E.; Dokholyan, N.V. Ab initio RNA folding by discrete molecular dynamics: From structure prediction to folding mechanisms. RNA 2008, 14, 1164–1173. [Google Scholar] [CrossRef]
  89. Jonikas, M.A.; Radmer, R.J.; Laederach, A.; Das, R.; Pearlman, S.; Herschlag, D.; Altman, R.B. Coarse-grained modeling of large RNA molecules with knowledge-based potentials and structural filters. RNA 2009, 15, 189–199. [Google Scholar] [CrossRef]
  90. Orsi, M.; Essex, J.W. The ELBA Force Field for Coarse-Grain Modeling of Lipid Membranes. PLoS ONE 2011, 6, e28637. [Google Scholar] [CrossRef]
  91. Shih, A.Y.; Arkhipov, A.; Freddolino, P.L.; Schulten, K. Coarse grained protein-lipid model with application to lipoprotein particles. J. Phys. Chem. B 2006, 110, 3674–3684. [Google Scholar] [CrossRef]
  92. Van Gunsteren, W.F.; Berendsen, H.J.C. Computer Simulation of Molecular Dynamics: Methodology, Applications, and Perspectives in Chemistry. Angew. Chem. Int. Ed. Engl. 1990, 29, 992–1023. [Google Scholar] [CrossRef]
  93. Dror, R.O.; Dirks, R.M.; Grossman, J.P.; Xu, H.; Shaw, D.E. Biomolecular simulation: A computational microscope for molecular biology. Annu. Rev. Biophys. 2012, 41, 429–452. [Google Scholar] [CrossRef]
  94. Hollingsworth, S.A.; Dror, R.O. Molecular Dynamics Simulation for All. Neuron 2018, 99, 1129–1143. [Google Scholar] [CrossRef] [PubMed]
  95. Arcon, J.P.; Defelipe, L.A.; Modenutti, C.P.; López, E.D.; Alvarez-Garcia, D.; Barril, X.; Turjanski, A.G.; Martí, M.A. Molecular Dynamics in Mixed Solvents Reveals Protein-Ligand Interactions, Improves Docking, and Allows Accurate Binding Free Energy Predictions. J. Chem. Inf. Modeling 2017, 57, 846–863. [Google Scholar] [CrossRef] [PubMed]
  96. Nair, P.C.; Miners, J.O. Molecular dynamics simulations: From structure function relationships to drug discovery. Silico Pharmacol. 2014, 2, 4. [Google Scholar] [CrossRef]
  97. Fernandez-Leiro, R.; Scheres, S.H. Unravelling biological macromolecules with cryo-electron microscopy. Nature 2016, 537, 339–346. [Google Scholar] [CrossRef]
  98. Xu, D.; Li, D. Molecular Dynamics Simulation Method. In Encyclopedia of Microfluidics and Nanofluidics; Li, D., Ed.; Springer: Boston, MA, USA, 2008; pp. 1391–1398. [Google Scholar]
  99. Mahdavi, A.; Jahandideh, S. Application of density similarities to predict membrane protein types based on pseudo-amino acid composition. J. Theor. Biol. 2011, 276, 132–137. [Google Scholar] [CrossRef]
  100. Nagle, J.F.; Tristram-Nagle, S. Structure of lipid bilayers. Biochim. Et Biophys. Acta 2000, 1469, 159–195. [Google Scholar] [CrossRef]
  101. Cuello, L.G.; Jogini, V.; Cortes, D.M.; Pan, A.C.; Gagnon, D.G.; Dalmas, O.; Cordero-Morales, J.F.; Chakrapani, S.; Roux, B.; Perozo, E. Structural basis for the coupling between activation and inactivation gates in K(+) channels. Nature 2010, 466, 272–275. [Google Scholar] [CrossRef]
  102. Shenoy, S.K.; Lefkowitz, R.J. β-Arrestin-mediated receptor trafficking and signal transduction. Trends Pharmacol. Sci. 2011, 32, 521–533. [Google Scholar] [CrossRef]
  103. Berman, H.M.; Westbrook, J.; Feng, Z.; Gilliland, G.; Bhat, T.N.; Weissig, H.; Shindyalov, I.N.; Bourne, P.E. The Protein Data Bank. Nucleic Acids Res. 2000, 28, 235–242. [Google Scholar] [CrossRef]
  104. Lomize, M.A.; Lomize, A.L.; Pogozheva, I.D.; Mosberg, H.I. OPM: Orientations of proteins in membranes database. Bioinformatics 2006, 22, 623–625. [Google Scholar] [CrossRef]
  105. Xu, Q.; Dunbrack, R.L., Jr. The protein common interface database (ProtCID)—A comprehensive database of interactions of homologous proteins in multiple crystal forms. Nucleic Acids Res. 2011, 39, D761–D770. [Google Scholar] [CrossRef] [PubMed]
  106. White, S.H. The progress of membrane protein structure determination. Protein Sci. 2004, 13, 1948–1949. [Google Scholar] [CrossRef]
  107. Kozma, D.; Simon, I.; Tusnády, G.E. PDBTM: Protein Data Bank of transmembrane proteins after 8 years. Nucleic Acids Res. 2013, 41, D524–D529. [Google Scholar] [CrossRef] [PubMed]
  108. Salomon-Ferrer, R.; Götz, A.W.; Poole, D.; Le Grand, S.; Walker, R.C. Routine Microsecond Molecular Dynamics Simulations with AMBER on GPUs. 2. Explicit Solvent Particle Mesh Ewald. J. Chem. Theory Comput. 2013, 9, 3878–3888. [Google Scholar] [CrossRef] [PubMed]
  109. Stone, J.E.; Hallock, M.J.; Phillips, J.C.; Peterson, J.R.; Luthey-Schulten, Z.; Schulten, K. Evaluation of Emerging Energy-Efficient Heterogeneous Computing Platforms for Biomolecular and Cellular Simulation Workloads. IEEE Int. Parallel Distrib. Processing Symp. Workshops 2016, 2016, 89–100. [Google Scholar] [CrossRef]
  110. Stone, J.E.; Sener, M.; Vandivort, K.L.; Barragan, A.; Singharoy, A.; Teo, I.; Ribeiro, J.V.; Isralewitz, B.; Liu, B.; Goh, B.C.; et al. Atomic detail visualization of photosynthetic membranes with GPU-accelerated ray tracing. Parallel Comput. 2016, 55, 17–27. [Google Scholar] [CrossRef]
  111. Marrink, S.-J.; Berendsen, H.J.C. Simulation of water transport through a lipid membrane. J. Phys. Chem. 1994, 98, 4155–4168. [Google Scholar] [CrossRef]
  112. Feller, S.E.; Venable, R.M.; Pastor, R.W. Computer Simulation of a DPPC Phospholipid Bilayer:  Structural Changes as a Function of Molecular Surface Area. Langmuir 1997, 13, 6555–6561. [Google Scholar] [CrossRef]
  113. Hansmann, U.H.E.; Okamoto, Y. Generalized-ensemble Monte Carlo method for systems with rough energy landscape. Phys. Rev. E 1997, 56, 2228–2233. [Google Scholar] [CrossRef]
  114. Lou, H.; Cukier, R.I. Molecular dynamics of apo-adenylate kinase: A distance replica exchange method for the free energy of conformational fluctuations. J. Phys. Chem. B 2006, 110, 24121–24137. [Google Scholar] [CrossRef]
  115. Im, W.; Brooks, C.L., 3rd. De novo folding of membrane proteins: An exploration of the structure and NMR properties of the fd coat protein. J. Mol. Biol. 2004, 337, 513–519. [Google Scholar] [CrossRef] [PubMed]
  116. Im, W.; Brooks, C.L. Interfacial folding and membrane insertion of designed peptides studied by molecular dynamics simulations. Proc. Natl. Acad. Sci. 2005, 102, 6771–6776. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  117. Nymeyer, H.; Woolf, T.B.; Garcia, A.E. Folding is not required for bilayer insertion: Replica exchange simulations of an alpha-helical peptide with an explicit lipid bilayer. Proteins 2005, 59, 783–790. [Google Scholar] [CrossRef]
  118. Mori, T.; Miyashita, N.; Im, W.; Feig, M.; Sugita, Y. Molecular dynamics simulations of biological membranes and membrane proteins using enhanced conformational sampling algorithms. Biochim. Et Biophys. Acta (BBA) Biomembr. 2016, 1858, 1635–1651. [Google Scholar] [CrossRef]
  119. Alexander, S.P.; Christopoulos, A.; Davenport, A.P.; Kelly, E.; Marrion, N.V.; Peters, J.A.; Faccenda, E.; Harding, S.D.; Pawson, A.J.; Sharman, J.L.; et al. THE CONCISE GUIDE TO PHARMACOLOGY 2017/18: G protein-coupled receptors. Br. J. Pharmacol. 2017, 174 (Suppl. S1), S17–S129. [Google Scholar] [CrossRef]
  120. Lundstrom, K. Latest development in drug discovery on G protein-coupled receptors. Curr. Protein Pept. Sci. 2006, 7, 465–470. [Google Scholar] [CrossRef]
  121. Overington, J.P.; Al-Lazikani, B.; Hopkins, A.L. How many drug targets are there? Nat. Rev. Drug Discov. 2006, 5, 993–996. [Google Scholar] [CrossRef] [PubMed]
  122. Heilker, R.; Wolff, M.; Tautermann, C.S.; Bieler, M. G-protein-coupled receptor-focused drug discovery using a target class platform approach. Drug Discov. Today 2009, 14, 231–240. [Google Scholar] [CrossRef]
  123. Mizuno, H.; Kihara, Y. Druggable Lipid GPCRs: Past, Present, and Prospects. In Druggable Lipid Signaling Pathways; Kihara, Y., Ed.; Springer International Publishing: Cham, Germany, 2020; pp. 223–258. [Google Scholar]
  124. Dror, R.O.; Pan, A.C.; Arlow, D.H.; Borhani, D.W.; Maragakis, P.; Shan, Y.; Xu, H.; Shaw, D.E. Pathway and mechanism of drug binding to G-protein-coupled receptors. Proc. Natl. Acad. Sci. USA 2011, 108, 13118–13123. [Google Scholar] [CrossRef] [PubMed]
  125. Marino, K.A.; Filizola, M. Investigating Small-Molecule Ligand Binding to G Protein-Coupled Receptors with Biased or Unbiased Molecular Dynamics Simulations. Methods Mol. Biol. 2018, 1705, 351–364. [Google Scholar] [CrossRef]
  126. Miao, Y.; McCammon, J.A. G-protein coupled receptors: Advances in simulation and drug discovery. Curr. Opin. Struct. Biol. 2016, 41, 83–89. [Google Scholar] [CrossRef] [PubMed]
  127. Huber, T.; Menon, S.; Sakmar, T.P. Structural basis for ligand binding and specificity in adrenergic receptors: Implications for GPCR-targeted drug discovery. Biochemistry 2008, 47, 11013–11023. [Google Scholar] [CrossRef] [PubMed]
  128. Cang, X.; Du, Y.; Mao, Y.; Wang, Y.; Yang, H.; Jiang, H. Mapping the functional binding sites of cholesterol in β2-adrenergic receptor by long-time molecular dynamics simulations. J. Phys. Chem. B 2013, 117, 1085–1094. [Google Scholar] [CrossRef] [PubMed]
  129. Dror, R.O.; Arlow, D.H.; Maragakis, P.; Mildorf, T.J.; Pan, A.C.; Xu, H.; Borhani, D.W.; Shaw, D.E. Activation mechanism of the β2-adrenergic receptor. Proc. Natl. Acad. Sci. USA 2011, 108, 18684–18689. [Google Scholar] [CrossRef]
  130. Shaw, D.E.; Deneroff, M.M.; Dror, R.O.; Kuskin, J.S.; Larson, R.H.; Salmon, J.K.; Young, C.; Batson, B.; Bowers, K.J.; Chao, J.C.; et al. Anton, a special-purpose machine for molecular dynamics simulation. Commun. ACM 2008, 51, 91–97. [Google Scholar] [CrossRef]
  131. Schneider, S.; Provasi, D.; Filizola, M. How Oliceridine (TRV-130) Binds and Stabilizes a μ-Opioid Receptor Conformational State That Selectively Triggers G Protein Signaling Pathways. Biochemistry 2016, 55, 6456–6466. [Google Scholar] [CrossRef] [PubMed]
  132. Lappano, R.; Maggiolini, M. G protein-coupled receptors: Novel targets for drug discovery in cancer. Nat. Rev. Drug Discov. 2011, 10, 47–60. [Google Scholar] [CrossRef]
  133. Jones, A.J.Y.; Gabriel, F.; Tandale, A.; Nietlispach, D. Structure and Dynamics of GPCRs in Lipid Membranes: Physical Principles and Experimental Approaches. Molecules 2020, 25, 4729. [Google Scholar] [CrossRef] [PubMed]
  134. Dror, R.O.; Green, H.F.; Valant, C.; Borhani, D.W.; Valcourt, J.R.; Pan, A.C.; Arlow, D.H.; Canals, M.; Lane, J.R.; Rahmani, R.; et al. Structural basis for modulation of a G-protein-coupled receptor by allosteric drugs. Nature 2013, 503, 295–299. [Google Scholar] [CrossRef] [PubMed]
  135. Stanley, N.; Pardo, L.; Fabritiis, G.D. The pathway of ligand entry from the membrane bilayer to a lipid G protein-coupled receptor. Sci. Rep. 2016, 6, 22639. [Google Scholar] [CrossRef]
  136. Laio, A.; Parrinello, M. Escaping free-energy minima. Proc. Natl. Acad. Sci. USA 2002, 99, 12562–12566. [Google Scholar] [CrossRef] [PubMed]
  137. Sabbadin, D.; Moro, S. Supervised molecular dynamics (SuMD) as a helpful tool to depict GPCR-ligand recognition pathway in a nanosecond time scale. J. Chem. Inf. Modeling 2014, 54, 372–376. [Google Scholar] [CrossRef]
  138. Dickson, C.J.; Hornak, V.; Velez-Vega, C.; McKay, D.J.; Reilly, J.; Sandham, D.A.; Shaw, D.; Fairhurst, R.A.; Charlton, S.J.; Sykes, D.A.; et al. Uncoupling the Structure-Activity Relationships of β2 Adrenergic Receptor Ligands from Membrane Binding. J. Med. Chem. 2016, 59, 5780–5789. [Google Scholar] [CrossRef]
  139. Hedger, G.; Sansom, M.S.P. Lipid interaction sites on channels, transporters and receptors: Recent insights from molecular dynamics simulations. Biochim. Et Biophys. Acta 2016, 1858, 2390–2400. [Google Scholar] [CrossRef] [PubMed]
  140. Lebon, G.; Warne, T.; Edwards, P.C.; Bennett, K.; Langmead, C.J.; Leslie, A.G.; Tate, C.G. Agonist-bound adenosine A2A receptor structures reveal common features of GPCR activation. Nature 2011, 474, 521–525. [Google Scholar] [CrossRef] [PubMed]
  141. Lee, J.Y.; Lyman, E. Predictions for cholesterol interaction sites on the A2A adenosine receptor. J. Am. Chem. Soc. 2012, 134, 16512–16515. [Google Scholar] [CrossRef] [PubMed]
  142. Neale, C.; Herce, H.D.; Pomès, R.; García, A.E. Can Specific Protein-Lipid Interactions Stabilize an Active State of the Beta 2 Adrenergic Receptor? Biophys. J. 2015, 109, 1652–1662. [Google Scholar] [CrossRef]
  143. Dawaliby, R.; Trubbia, C.; Delporte, C.; Masureel, M.; Van Antwerpen, P.; Kobilka, B.K.; Govaerts, C. Allosteric regulation of G protein-coupled receptor activity by phospholipids. Nat. Chem. Biol. 2016, 12, 35–39. [Google Scholar] [CrossRef]
  144. Kooistra, A.J.; Mordalski, S.; Pándy-Szekeres, G.; Esguerra, M.; Mamyrbekov, A.; Munk, C.; Keserű, G.M.; Gloriam, D.E. GPCRdb in 2021: Integrating GPCR sequence, structure and function. Nucleic Acids Res. 2020, 49, D335–D343. [Google Scholar] [CrossRef]
  145. GPCRs: G Protein Coupled Receptors Database. Available online: https://gproteindb.org (accessed on 13 August 2022).
  146. Da Costa, C.H.S.; de Freitas, C.A.B.; Alves, C.N.; Lameira, J. Assessment of mutations on RBD in the Spike protein of SARS-CoV-2 Alpha, Delta and Omicron variants. Sci. Rep. 2022, 12, 8540. [Google Scholar] [CrossRef]
  147. Harvey, W.T.; Carabelli, A.M.; Jackson, B.; Gupta, R.K.; Thomson, E.C.; Harrison, E.M.; Ludden, C.; Reeve, R.; Rambaut, A.; Peacock, S.J.; et al. SARS-CoV-2 variants, spike mutations and immune escape. Nat. Rev. Microbiol. 2021, 19, 409–424. [Google Scholar] [CrossRef] [PubMed]
  148. Padhi, A.K.; Rath, S.L.; Tripathi, T. Accelerating COVID-19 Research Using Molecular Dynamics Simulation. J. Phys. Chem. B 2021, 125, 9078–9091. [Google Scholar] [CrossRef] [PubMed]
  149. Sinha, S.; Tam, B.; Wang, S.M. RBD Double Mutations of SARS-CoV-2 Strains Increase Transmissibility through Enhanced Interaction between RBD and ACE2 Receptor. Viruses 2021, 14, 1. [Google Scholar] [CrossRef] [PubMed]
  150. Amaro, R.E.; Mulholland, A.J. Biomolecular Simulations in the Time of COVID-19, and After. Comput. Sci. Eng. 2020, 22, 30–36. [Google Scholar] [CrossRef] [PubMed]
  151. Casalino, L.; Gaieb, Z.; Goldsmith, J.A.; Hjorth, C.K.; Dommer, A.C.; Harbison, A.M.; Fogarty, C.A.; Barros, E.P.; Taylor, B.C.; McLellan, J.S.; et al. Beyond Shielding: The Roles of Glycans in the SARS-CoV-2 Spike Protein. ACS Cent. Sci. 2020, 6, 1722–1734. [Google Scholar] [CrossRef]
  152. Komatsu, T.S.; Koyama, Y.; Okimoto, N.; Morimoto, G.; Ohno, Y.; Taiji, M. COVID-19 related trajectory data of 10 microseconds all atom molecular dynamics simulation of SARS-CoV-2 dimeric main protease. Mendeley Data 2020. [Google Scholar] [CrossRef]
  153. Acharya, A.; Agarwal, R.; Baker, M.B.; Baudry, J.; Bhowmik, D.; Boehm, S.; Byler, K.G.; Chen, S.Y.; Coates, L.; Cooper, C.J.; et al. Supercomputer-Based Ensemble Docking Drug Discovery Pipeline with Application to Covid-19. J. Chem. Inf. Modeling 2020, 60, 5832–5852. [Google Scholar] [CrossRef]
  154. Zimmerman, M.I.; Porter, J.R.; Ward, M.D.; Singh, S.; Vithani, N.; Meller, A.; Mallimadugula, U.L.; Kuhn, C.E.; Borowsky, J.H.; Wiewiora, R.P.; et al. SARS-CoV-2 simulations go exascale to predict dramatic spike opening and cryptic pockets across the proteome. Nat. Chem. 2021, 13, 651–659. [Google Scholar] [CrossRef]
  155. Li, B.; Deng, A.; Li, K.; Hu, Y.; Li, Z.; Shi, Y.; Xiong, Q.; Liu, Z.; Guo, Q.; Zou, L.; et al. Viral infection and transmission in a large, well-traced outbreak caused by the SARS-CoV-2 Delta variant. Nat. Commun. 2022, 13, 460. [Google Scholar] [CrossRef]
  156. Silva, S.; Pena, L. Collapse of the public health system and the emergence of new variants during the second wave of the COVID-19 pandemic in Brazil. One Health 2021, 13, 100287. [Google Scholar] [CrossRef]
  157. Ho, D.; Wang, P.; Liu, L.; Iketani, S.; Luo, Y.; Guo, Y.; Wang, M.; Yu, J.; Zhang, B.; Kwong, P.; et al. Increased Resistance of SARS-CoV-2 Variants B.1.351 and B.1.1.7 to Antibody Neutralization. Res. Sq. 2021. [Google Scholar] [CrossRef]
  158. Demoliner, M.; da Silva, M.S.; Gularte, J.S.; Hansen, A.W.; de Almeida, P.R.; Weber, M.N.; Heldt, F.H.; Silveira, F.; Filippi, M.; de Abreu Góes Pereira, V.M.; et al. Predominance of SARS-CoV-2 P.1 (Gamma) lineage inducing the recent COVID-19 wave in southern Brazil and the finding of an additional S: D614A mutation. Infect. Genet. Evol. 2021, 96, 105134. [Google Scholar] [CrossRef] [PubMed]
  159. Luan, B.; Wang, H.; Huynh, T. Enhanced binding of the N501Y-mutated SARS-CoV-2 spike protein to the human ACE2 receptor: Insights from molecular dynamics simulations. FEBS Lett. 2021, 595, 1454–1461. [Google Scholar] [CrossRef] [PubMed]
  160. Teruel, N.; Mailhot, O.; Najmanovich, R.J. Modelling conformational state dynamics and its role on infection for SARS-CoV-2 Spike protein variants. PLOS Comput. Biol. 2021, 17, e1009286. [Google Scholar] [CrossRef]
  161. Ali, F.; Kasry, A.; Amin, M. The new SARS-CoV-2 strain shows a stronger binding affinity to ACE2 due to N501Y mutant. Med. Drug Discov. 2021, 10, 100086. [Google Scholar] [CrossRef]
  162. Chakraborty, S. E484K and N501Y SARS-CoV 2 spike mutants Increase ACE2 recognition but reduce affinity for neutralizing antibody. Int. Immunopharmacol. 2022, 102, 108424. [Google Scholar] [CrossRef]
  163. Barton, M.I.; MacGowan, S.A.; Kutuzov, M.A.; Dushek, O.; Barton, G.J.; van der Merwe, P.A. Effects of common mutations in the SARS-CoV-2 Spike RBD and its ligand, the human ACE2 receptor on binding affinity and kinetics. eLife 2021, 10, e70658. [Google Scholar] [CrossRef]
  164. Li, Q.; Nie, J.; Wu, J.; Zhang, L.; Ding, R.; Wang, H.; Zhang, Y.; Li, T.; Liu, S.; Zhang, M.; et al. SARS-CoV-2 501Y.V2 variants lack higher infectivity but do have immune escape. Cell 2021, 184, 2362–2371.e2369. [Google Scholar] [CrossRef]
  165. Zhao, S.; Lou, J.; Chong, M.K.C.; Cao, L.; Zheng, H.; Chen, Z.; Chan, R.W.Y.; Zee, B.C.Y.; Chan, P.K.S.; Wang, M.H. Inferring the Association between the Risk of COVID-19 Case Fatality and N501Y Substitution in SARS-CoV-2. Viruses 2021, 13, 638. [Google Scholar] [CrossRef]
  166. Istifli, E.S.; Netz, P.A.; Sihoglu Tepe, A.; Sarikurkcu, C.; Tepe, B. Understanding the molecular interaction of SARS-CoV-2 spike mutants with ACE2 (angiotensin converting enzyme 2). J. Biomol. Struct. Dyn. 2021, 1–12. [Google Scholar] [CrossRef]
  167. Collier, D.A.; De Marco, A.; Ferreira, I.A.T.M.; Meng, B.; Datir, R.P.; Walls, A.C.; Kemp, S.A.; Bassi, J.; Pinto, D.; Silacci-Fregni, C.; et al. Sensitivity of SARS-CoV-2 B.1.1.7 to mRNA vaccine-elicited antibodies. Nature 2021, 593, 136–141. [Google Scholar] [CrossRef]
  168. Jangra, S.; Ye, C.; Rathnasinghe, R.; Stadlbauer, D.; Krammer, F.; Simon, V.; Martinez-Sobrido, L.; García-Sastre, A.; Schotsaert, M. SARS-CoV-2 spike E484K mutation reduces antibody neutralisation. Lancet Microbe 2021, 2, e283–e284. [Google Scholar] [CrossRef]
  169. Hospital, A.; Andrio, P.; Cugnasco, C.; Codo, L.; Becerra, Y.; Dans, P.D.; Battistini, F.; Torres, J.; Goñi, R.; Orozco, M.; et al. BIGNASim: A NoSQL database structure and analysis portal for nucleic acids simulation data. Nucleic Acids Res. 2016, 44, D272–D278. [Google Scholar] [CrossRef] [PubMed]
  170. Thibault, J.C.; Cheatham, T.E.; Facelli, J.C. iBIOMES Lite: Summarizing Biomolecular Simulation Data in Limited Settings. J. Chem. Inf. Modeling 2014, 54, 1810–1819. [Google Scholar] [CrossRef] [PubMed]
  171. Tai, K.; Murdock, S.; Wu, B.; Ng, M.H.; Johnston, S.; Fangohr, H.; Cox, S.J.; Jeffreys, P.; Essex, J.W.; Sansom, M.S. BioSimGrid: Towards a worldwide repository for biomolecular simulations. Org. Biomol. Chem. 2004, 2, 3219–3221. [Google Scholar] [CrossRef] [PubMed]
  172. Meyer, T.; D’Abramo, M.; Hospital, A.; Rueda, M.; Ferrer-Costa, C.; Pérez, A.; Carrillo, O.; Camps, J.; Fenollosa, C.; Repchevsky, D.; et al. MoDEL (Molecular Dynamics Extended Library): A database of atomistic molecular dynamics trajectories. Structure 2010, 18, 1399–1409. [Google Scholar] [CrossRef]
  173. Feig, M.; Nawrocki, G.; Yu, I.; Wang, P.-h.; Sugita, Y. Challenges and opportunities in connecting simulations with experiments via molecular dynamics of cellular environments. J. Phys. Conf. Ser. 2018, 1036, 012010. [Google Scholar] [CrossRef]
  174. Petrov, D.; Zagrovic, B. Are Current Atomistic Force Fields Accurate Enough to Study Proteins in Crowded Environments? PLOS Comput. Biol. 2014, 10, e1003638. [Google Scholar] [CrossRef]
  175. Lin, H.; Truhlar, D.G. QM/MM: What have we learned, where are we, and where do we go from here? Theor. Chem. Acc. 2007, 117, 185–199. [Google Scholar] [CrossRef]
  176. Botu, V.; Ramprasad, R. Adaptive machine learning framework to accelerate ab initio molecular dynamics. Int. J. Quantum Chem. 2015, 115, 1074–1083. [Google Scholar] [CrossRef]
  177. Wang, J.; Olsson, S.; Wehmeyer, C.; Pérez, A.; Charron, N.E.; de Fabritiis, G.; Noé, F.; Clementi, C. Machine Learning of Coarse-Grained Molecular Dynamics Force Fields. ACS Cent. Sci. 2019, 5, 755–767. [Google Scholar] [CrossRef] [PubMed]
  178. Li, Z.; Kermode, J.R.; De Vita, A. Molecular Dynamics with On-the-Fly Machine Learning of Quantum-Mechanical Forces. Phys. Rev. Lett. 2015, 114, 096405. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  179. Westermayr, J.; Gastegger, M.; Menger, M.F.S.J.; Mai, S.; González, L.; Marquetand, P. Machine learning enables long time scale molecular photodynamics simulations. Chem. Sci. 2019, 10, 8100–8107. [Google Scholar] [CrossRef] [PubMed]
  180. Shen, C.; Ding, J.; Wang, Z.; Cao, D.; Ding, X.; Hou, T. From machine learning to deep learning: Advances in scoring functions for protein–ligand docking. WIREs Comput. Mol. Sci. 2020, 10, e1429. [Google Scholar] [CrossRef]
  181. Casadio, R.; Martelli, P.L.; Savojardo, C. Machine learning solutions for predicting protein–protein interactions. Wiley Interdiscip. Rev. Comput. Mol. Sci. 2022, e1618. [Google Scholar] [CrossRef]
  182. Arantes, P.R.; Polêto, M.D.; Pedebos, C.; Ligabue-Braun, R. Making it Rain: Cloud-Based Molecular Simulations for Everyone. J. Chem. Inf. Modeling 2021, 61, 4852–4856. [Google Scholar] [CrossRef]
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Figure 1. The growing use of MD Simulation studies over the years as reflected by publication (1980–2021). Data was from Web of Science.
Figure 1. The growing use of MD Simulation studies over the years as reflected by publication (1980–2021). Data was from Web of Science.
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Figure 2. The Molecular Simulations timeline showing the breakthrough achievements in MD Simulation studies.
Figure 2. The Molecular Simulations timeline showing the breakthrough achievements in MD Simulation studies.
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Figure 3. The RBD domain of SARS-CoV-2 spike protein showing the mutations in Alpha, Beta, Gamma, Delta and Omicron mutants.
Figure 3. The RBD domain of SARS-CoV-2 spike protein showing the mutations in Alpha, Beta, Gamma, Delta and Omicron mutants.
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Figure 4. The change in antibody binding sites in the double mutants L452R/T478K (Delta), L452R/E484Q (kappa) and E484K/N501Y (Beta) compared with native antibody binding sites.
Figure 4. The change in antibody binding sites in the double mutants L452R/T478K (Delta), L452R/E484Q (kappa) and E484K/N501Y (Beta) compared with native antibody binding sites.
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Table
Table 1. Atomistic and coarse-grained forcefield in MD Simulations.
Table 1. Atomistic and coarse-grained forcefield in MD Simulations.
No.ForcefieldDrugsLipidDNA & RNAProtein
1GROMOSGROMOS 43A1, GROMOS 45A3/4, GROMOS53A5/6, GROMOS54A7, GROMOS54B7, GROMOS54A8
2OPLSOPLS-AAOPLS-AAOPLS-AA/MOPLS-AA, OPLS-AA/L
3CHARMMCHARMM general force field (CGenFF)CHARMM27 lipids, CHARMM36 lipidsCHARMM27 DNA, CHARMM27 RNA/DNA, CHARMM 36 RNA, CHARMM 36 DNACHARMM22/CMAP, CHARM27, CHARMM36, CHARMM36m
4AMBERGeneral AMBER force field (GAFF)LIPID14, LIPID21AMBER99 OL3, AMBER99bsc, AMBER OL15AMBER94, AMBER96, AMBER99, AMBER99sb, AMBER03, AMBER14sb, AMBER15ipq, AMBER19sb
5MARTINIMARTINI 2, MARTINI22, MARTINI22p, MARTINI 3, MARTINI dry, MARTINI ELNEDYN22, MARTINI ELNEDYNP22MARTINI 2, MARTINI22, MARTINI22p, MARTINI 3, MARTINI-Dry, MARTINI ELNEDYN22, MARTINI ELNEDYNP22MARTINI 2015MARTINI 2, MARTINI22, MARTINI22p, MARTINI 3, MARTINI dry, MARTINI ELNEDYN22, MARTINI ELNEDYNP22
6Coarse-grained forcefield models (additional)-Electrostatics-based model (ELBA) [90]
protein-lipid CG model [91]		PRIMONA, DMD, NAST, ENMs, oxRNA, SimRNA, SPQRRosetta centroid (CEN), UNRES, CABS, PRIMO, AWSEM, SURPASS, Scorpion, OPEP
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Sinha, S.; Tam, B.; Wang, S.M. Applications of Molecular Dynamics Simulation in Protein Study. Membranes 2022, 12, 844. https://doi.org/10.3390/membranes12090844

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