Relative Free Energies Research Articles

Theoretical investigating mechanisms of drug-resistance generated by mutation-induced changes in influenza viruses

Influenza A (A/HxNy) is a significant public health concern due to its high infectiousness and mortality. Neuraminidase, which interacts with sialic acid (SIA) in host cells, has become an essential target since its highly conserved catalytic center structure, while resistance mutations have already generated. Here, a detailed investigation of the drug resistance mechanism caused by mutations was performed for subtype N9 (A/H7N9). Molecular dynamics simulation and alanine-scanning-interaction-entropy method (ASIE) were used to explore the critical differences between N9 and Zanamivir (ZMR) before and after R294K mutation. The results showed that the mutation caused the hydrogen bond between Arg294 and ZMR to break, then the hydrogen bonding network was disrupted, leading to weakened binding ability and resistance. While in wild type (A/H7N9WT), this hydrogen bond was initially stable. Mean-while, N9 derived from A/H11N9 was obtained as an R292K mutation. Then the relative binding free energy of N9 with five inhibitors (SIA, DAN, ZMR, G28, and G39) was predicted, basically consistent with experimental values, indicating that the calculated results were reliable by ASIE. In addition, Arg292 and Tyr406 were hot spots in the A/H11N9WT-drugs. However, Lys292 was not observed as a favorable contributing residue in A/H11N9R292K, which may promote resistance. In comparison, Tyr406 remained the hotspot feature when SIA, ZMR, and G28 binding to A/H11N9R292K. Combining the two groups, we speculate that the resistance was mainly caused by the disruption of the hydrogen bonding network and the transformation of hotspots. This study could guide novel drug delivery of drug-resistant mutations in the treatment of A/HxN9.

Predicting and Simulating Mutational Effects on Protein Folding Kinetics.

Mutational perturbations of protein structures, i.e., phi-value analysis, are commonly employed to probe the extent of involvement of a particular residue in the rate-determining step(s) of folding. This generally involves the measurement of folding thermodynamic parameters and kinetic rate constants for the wild-type and mutant proteins. While computational approaches have been reasonably successful in understanding and predicting the effect of mutations on folding thermodynamics, it has been challenging to explore the same on kinetics due to confounding structural, energetic, and dynamic factors. Accordingly, the frequent observation of fractional phi-values (mean of ~0.3) has resisted a precise and consistent interpretation. Here, we describe how to construct, parameterize, and employ a simple one-dimensional free energy surface model that is grounded in the basic tenets of the energy landscape theory to predict and simulate the effect of mutations on folding kinetics. As a proof of principle, we simulate one-dimensional free energy profiles of 806 mutations from 24 different proteins employing just the experimental destabilization as input, reproduce the relative unfolding activation free energies with a correlation of 0.91, and show that the mean phi-value of 0.3 essentially corresponds to the extent of stabilization energy gained at the barrier top while folding.

Relative Binding Free-Energy Calculations at Lipid-Exposed Sites: Deciphering Hot Spots

Relative binding free-energy (RBFE) calculations are experiencing resurgence in the computer-aided drug design of novel small molecules due to performance gains allowed by cutting-edge molecular mechanic force fields and computer hardware. Application of RBFE to soluble proteins is becoming a routine, while recent studies outline necessary steps to successfully apply RBFE at the orthosteric site of membrane-embedded G-protein-coupled receptors (GPCRs). In this work, we apply RBFE to a congeneric series of antagonists that bind to a lipid-exposed, extra-helical site of the P2Y1 receptor. We find promising performance of RBFE, such that it may be applied in a predictive manner on drug discovery programs targeting lipid-exposed sites. Further, by the application of the microkinetic model, binding at a lipid-exposed site can be split into (1) membrane partitioning of the drug molecule followed by (2) binding at the extra-helical site. We find that RBFE can be applied to calculate the free energy of each step, allowing the uncoupling of observed binding free energy from the influence of membrane affinity. This protocol may be used to identify binding hot spots at extra-helical sites and guide drug discovery programs toward optimizing intrinsic activity at the target.

Barrierless HNO3 formation from the hydrolysis reaction of NO2 with Cl atom in the atmosphere

Nitric acid (HNO3) is one of the main pollutants in the atmosphere and has an important effect on the air pollution. However, the formation pathways of HNO3 are still poorly understood in the marine boundary layer (MBL). Here, we show evidence of the formation of HNO3 from chlorine (Cl) radical and nitrogen dioxide (NO2), with 1–3 water molecules, as well as on the air-water interface, based on the direct Born-Oppenheimer molecular dynamics (BOMD) simulation. The relative free energy change along the reaction coordinates for the (Cl)(NO2)(H2O)1 (monohydrate) system was calculated using thermodynamic integration, revealing that the monohydrate system exhibited no free energy barrier and was 26.5 kcal mol−1 exergonic. This suggests that the formation of HNO3 from the monohydrate system is thermodynamically favorable. For all the systems studied, the formation of HNO3 was directly observed using BOMD simulations, and only one water molecule participates in the reaction. However, loop structures, which were expected to be ubiquitous on the air-water interface, were not observed. This study identifies the importance of the reactions of Cl atom, NO2, and water molecules in the formation of HNO3 in the MBL region.

Toward Automated Sampling of Polymorph Nucleation and Free Energies with the SGOOP and Metadynamics.

Understanding the driving forces behind the nucleation of different polymorphs is of great importance for material sciences and the pharmaceutical industry. This includes understanding the reaction coordinate that governs the nucleation process and correctly calculating the relative free energies of different polymorphs. Here, we demonstrate, for the prototypical case of urea nucleation from the melt, how one can learn such a one-dimensional reaction coordinate as a function of prespecified order parameters and use it to perform efficient biased all-atom molecular dynamics simulations. The reaction coordinate is learnt as a function of the generic thermodynamic and structural order parameters using the "spectral gap optimization of order parameters (SGOOP)" approach [Tiwary, P. and Berne, B. J. Proc. Natl. Acad. Sci. U.S.A. (2016)] and is biased using well-tempered metadynamics simulations. The reaction coordinate gives insights into the role played by different structural and thermodynamics order parameters, and the biased simulations obtain accurate relative free energies for different polymorphs. This includes an accurate prediction of the approximate pressure at which urea undergoes a phase transition and one of the metastable polymorphs becomes the most stable conformation. We believe the ideas demonstrated in this work will facilitate efficient sampling of nucleation in complex, generic systems.

Rh(II)‐mediated one‐pot synthesis of dihydrobenzofuran and spiro[2.5]oct‐1‐ene: Experimental and DFT studies

AbstractThis study represents an experimental and computational approach to investigate the rhodium‐catalyzed one‐pot synthesis of dihydrobenzofuran‐4‐one (DBF) and spiro[2.5]oct‐1‐ene (SOE) derivatives. Density functional theory (DFT) calculations were performed at B3LYP and M06‐2X level theory. For mechanistic studies, the calculation employing B3LYP/GenECP/LanL2DZ/6‐311++G(d,p) level of theory demonstrated that a [3 + 2] cycloaddition reaction between diazo compound and phenylacetylene (PhA) proceeds through a two‐step mechanism via a barrierless and highly exergonic process with relative free energy 73.61 kcal/mol to yield the kinetically favored DBF derivatives (50%–62.5%). In contrast, the assemble of SOE derivatives follows [2 + 1] cycloaddition between in situ generated cyclohexane‐1,3‐dione carbene‐2 and PhA, with the potential energy barrier 4.41 kJ/mol. Thermochemistry calculation disclosed that the cycloaddition reactions are spontaneous, and DBF (6a) is thermodynamically more stable than its constitutional isomer SOE (7a) by 42.59 kcal/mol. However, natural bond orbital (NBO), HOMO–LUMO energy gaps (4.62–4.89 eV), dipole moments, polarizability, first‐order hyperpolarizability, and global reactivity descriptors were calculated to understand products' structural features. Additionally, Merck Molecular Force Field (MMFF94), followed by the B3LYP level of theory, was applied to predict the relative stability for the various conformations of 6b and 7b. The Boltzmann weighted average 1H chemical shift computed by GIAO‐B3LYP/6‐311+(2d,p) method and UV‐Vis absorption calculated using time‐dependent density functional theory (TD‐DFT) agree with experimental results. Finally, the synthesis of DBF and SOE derivatives is herein illustrated.

O2 and Other High-Energy Molecules in Photosynthesis: Why Plants Need Two Photosystems.

The energetics of photosynthesis in plants have been re-analyzed in a framework that represents the relatively high energy of O2 correctly. Starting with the photon energy exciting P680 and “loosening an electron”, the energy transfer and electron transport are represented in a comprehensive, self-explanatory sequence of redox energy transfer and release diagrams. The resulting expanded Z-scheme explicitly shows charge separation as well as important high-energy species such as O2, TyrZ˙, and P680+˙, whose energies are not apparent in the classical Z-scheme of photosynthesis. Crucially, the energetics of the three important forms of P680 and of P700 are clarified. The relative free energies of oxidized and reduced species are shown explicitly in kJ/mol, not encrypted in volts. Of the chemical energy produced in photosynthesis, more is stored in O2 than in glucose. The expanded Z-scheme introduced here provides explanatory power lacking in the classical scheme. It shows that P680* is energetically boosted to P680+˙ by the favorable electron affinity of pheophytin and that Photosystem I (PSI) has insufficient energy to split H2O and produce O2 because P700* is too easily ionized. It also avoids the Z-scheme’s bewildering implication, according to the “electron waterfall” concept, that H2O gives off electrons that spontaneously flow to chlorophyll while releasing energy. The new analysis explains convincingly why plants need two different photosystems in tandem: (i) PSII mostly extracts hydrogen from H2O, producing PQH2 (plastoquinol), and generates the energetically expensive product O2; this step provides little energy directly to the plant; (ii) PSI produces chemical energy for the organism, by pumping protons against a concentration gradient and producing less reluctant hydrogen donors. It also documents that electron transport and energy transfer occur in opposite directions and do not involve redox voltages. The analysis makes it clear that the high-energy species in photosynthesis are unstable, electron-deficient species such as P680+˙ and TyrZ˙, not putative high-energy electrons.

Drug Repurposing to Identify Nilotinib as a Potential SARS-CoV-2 Main Protease Inhibitor: Insights from a Computational and In Vitro Study.

COVID-19, an acute viral pneumonia, has emerged as a devastating pandemic. Drug repurposing allows researchers to find different indications of FDA-approved or investigational drugs. In this current study, a sequence of pharmacophore and molecular modeling-based screening against COVID-19 Mpro (PDB: 6LU7) suggested a subset of drugs, from the Drug Bank database, which may have antiviral activity. A total of 44 out of 8823 of the most promising virtual hits from the Drug Bank were subjected to molecular dynamics simulation experiments to explore the strength of their interactions with the SARS-CoV-2 Mpro active site. MD findings point toward three drugs (DB04020, DB12411, and DB11779) with very low relative free energies for SARS-CoV-2 Mpro with interactions at His41 and Met49. MD simulations identified an additional interaction with Glu166, which enhanced the binding affinity significantly. Therefore, Glu166 could be an interesting target for structure-based drug design. Quantitative structural–activity relationship analysis was performed on the 44 most promising hits from molecular docking-based virtual screening. Partial least square regression accurately predicted the values of independent drug candidates’ binding energy with impressively high accuracy. Finally, the EC50 and CC50 of 10 drug candidates were measured against SARS-CoV-2 in cell culture. Nilotinib and bemcentinib had EC50 values of 2.6 and 1.1 μM, respectively. In summary, the results of our computer-aided drug design provide a roadmap for rational drug design of Mpro inhibitors and the discovery of certified medications as COVID-19 antiviral therapeutics.

Gmx_MMPBSA: A New Tool to Perform End-State Free Energy Calculations with GROMACS

Molecular mechanics/Poisson-Boltzmann (Generalized-Born) surface area is one of the most popular methods to estimate binding free energies. This method has been proven to balance accuracy and computational efficiency, especially when dealing with large systems. As a result of its popularity, several programs have been developed for performing MM/PB(GB)SA calculations within the GROMACS community. These programs, however, present several limitations. Here we present gmx_MMPBSA, a new tool to perform end-state free energy calculations from GROMACS molecular dynamics trajectories. gmx_MMPBSA provides the user with several options, including binding free energy calculations with different solvation models (PB, GB, or 3D-RISM), stability calculations, computational alanine scanning, entropy corrections, and binding free energy decomposition. Noteworthy, several promising methodologies to calculate relative binding free energies such as alanine scanning with variable dielectric constant and interaction entropy have also been implemented in gmx_MMPBSA. Two additional tools-gmx_MMPBSA_test and gmx_MMPBSA_ana-have been integrated within gmx_MMPBSA to improve its usability. Multiple illustrating examples can be accessed through gmx_MMPBSA_test, while gmx_MMPBSA_ana provides fast, easy, and efficient access to different graphics plotted from gmx_MMPBSA output files. The latest version (v1.4.3, 26/05/2021) is available free of charge (documentation, test files, and tutorials included) at https://github.com/Valdes-Tresanco-MS/gmx_MMPBSA.

Significant Insight into the Origin of Reaction Barriers Determining Dihydrogen Activation by G13-P-P (G13 = Group 13 Element) and G15-P-Ga (G15 = Group 15 Element) Frustrated Lewis Pairs.

The heterolytic cleavage of H2 by multiply bonded phosphorus-bridged G13-P-P-Rea (G13 = B, Al, Ga, In, and Tl) and G15-P-Ga-Rea (G15 = N, P, As, Sb, and Bi) frustrated Lewis pairs (FLPs) has been theoretically investigated using density functional theory calculations. For the above nine FLP-type molecules, our theoretical findings suggest that only Al-P-P-Rea, Ga-P-P-Rea, and In-P-P-Rea can undergo the energetically feasible H2 activation reaction from kinetic and thermodynamic viewpoints. Our study based on the activation strain model (ASM) reveals that gaining a better orbital overlap between G13-P-P-Rea and G15-P-Ga-Rea molecules and H2 affected the reaction barriers through the atomic radius of G13 and G15. According to our energy decomposition analysis-natural orbitals for chemical valence (EDA-NOCV) results, the bonding of these H2 activation reactions involving G13-P-P-Rea and G15-P-Ga-Rea is dominated by the donor-acceptor interaction (singlet-singlet interaction) rather than the electron-sharing interaction (triplet-triplet interaction). Moreover, our EDA-NOCV evidence reveals that the best description for the above bonding situations is the lone pair(G15) → σ*(H2) interaction rather than the empty p-π-orbital(G13) ← σ(H2) interaction. In particular, the findings in this work based on theoretically calculated geometries and the corresponding relative free energies of the stationary points combined with the results from the above sophisticated methods nicely agree with the famous Hammond postulate.

Development and Benchmarking of Open Force Field v1.0.0-the Parsley Small-Molecule Force Field.

We present a methodology for defining and optimizing a general force field for classical molecular simulations, and we describe its use to derive the Open Force Field 1.0.0 small-molecule force field, codenamed Parsley. Rather than using traditional atom typing, our approach is built on the SMIRKS-native Open Force Field (SMIRNOFF) parameter assignment formalism, which handles increases in the diversity and specificity of the force field definition without needlessly increasing the complexity of the specification. Parameters are optimized with the ForceBalance tool, based on reference quantum chemical data that include torsion potential energy profiles, optimized gas-phase structures, and vibrational frequencies. These quantum reference data are computed and are maintained with QCArchive, an open-source and freely available distributed computing and database software ecosystem. In this initial application of the method, we present essentially a full optimization of all valence parameters and report tests of the resulting force field against compounds and data types outside the training set. These tests show improvements in optimized geometries and conformational energetics and demonstrate that Parsley's accuracy for liquid properties is similar to that of other general force fields, as is accuracy on binding free energies. We find that this initial Parsley force field affords accuracy similar to that of other general force fields when used to calculate relative binding free energies spanning 199 protein-ligand systems. Additionally, the resulting infrastructure allows us to rapidly optimize an entirely new force field with minimal human intervention.

FEPrepare: A Web-Based Tool for Automating the Setup of Relative Binding Free Energy Calculations.

Relative binding free energy calculations in drug design are becoming a useful tool in facilitating lead binding affinity optimization in a cost- and time-efficient manner. However, they have been limited by technical challenges such as the manual creation of large numbers of input files to set up, run, and analyze free energy simulations. In this Application Note, we describe FEPrepare, a novel web-based tool, which automates the setup procedure for relative binding FEP calculations for the dual-topology scheme of NAMD, one of the major MD engines, using OPLS-AA force field topology and parameter files. FEPrepare provides the user with all necessary files needed to run a FEP/MD simulation with NAMD. FEPrepare can be accessed and used at https://feprepare.vi-seem.eu/.

Expanded Ensemble Methods Can be Used to Accurately Predict Protein-Ligand Relative Binding Free Energies.

Alchemical free energy methods have become indispensable in computational drug discovery for their ability to calculate highly accurate estimates of protein-ligand affinities. Expanded ensemble (EE) methods, which involve single simulations visiting all of the alchemical intermediates, have some key advantages for alchemical free energy calculation. However, there have been relatively few examples published in the literature of using expanded ensemble simulations for free energies of protein-ligand binding. In this paper, as a test of expanded ensemble methods, we compute relative binding free energies using the Open Force Field Initiative force field (codename "Parsley") for 24 pairs of Tyk2 inhibitors derived from a congeneric series of 16 compounds. The EE predictions agree well with the experimental values (root-mean-square error (RMSE) of 0.94 ± 0.13 kcal mol-1 and mean unsigned error (MUE) of 0.75 ± 0.12 kcal mol-1). We find that while increasing the number of alchemical intermediates can improve the phase space overlap, faster convergence can be obtained with fewer intermediates, as long as acceptance rates are sufficient. We also find that convergence can be improved using more aggressive updating of biases, and that estimates can be improved by performing multiple independent EE calculations. This work demonstrates that EE is a viable option for alchemical free energy calculation. We discuss the implications of these findings for rational drug design, as well as future directions for improvement.

Predicting the Relative Binding Affinity for Reversible Covalent Inhibitors by Free Energy Perturbation Calculations.

Covalent inhibitors are assuming central importance in drug discovery projects, especially in this pandemic scenario. Many research groups have focused their attention on inhibiting viral proteases or human proteases such as cathepsin L (hCatL). The inhibition of these critical enzymes may impair viral replication. However, molecular modeling of covalent ligands is challenging since covalent and noncovalent ligand-bound states must be considered in the binding process. In this work, we evaluated the suitability of free energy perturbation (FEP) calculations as a tool for predicting the binding affinity of reversible covalent inhibitors of hCatL. Our strategy relies on the relative free energy calculated for both covalent and noncovalent complexes and the free energy changes have been compared with experimental data for eight nitrile-based inhibitors, including three new inhibitors of hCatL. Our results demonstrate that the covalent complex can be employed to properly rank the inhibitors. Nevertheless, a comparison of the free energy changes in both noncovalent and covalent states is valuable to interpret the effect triggered by the formation of the covalent bond on the interactions played by functional groups distant from the warhead. Overall, FEP can be employed as a powerful predictor tool in developing and understanding the activity of reversible covalent inhibitors.

Perturbation Free-Energy Toolkit: An Automated Alchemical Topology Builder.

Free-energy calculations play an important role in the application of computational chemistry to a range of fields, including protein biochemistry, rational drug design, or materials science. Importantly, the free-energy difference is directly related to experimentally measurable quantities such as partition and adsorption coefficients, water activity, and binding affinities. Among several techniques aimed at predicting free-energy differences, perturbation approaches, involving the alchemical transformation of one molecule into another through intermediate states, stand out as rigorous methods based on statistical mechanics. However, despite the importance of free-energy calculations, the applicability of the perturbation approaches is still largely impeded by a number of challenges, including the definition of the perturbation path, i.e., alchemical changes leading to the transformation of one molecule to the other. To address this, an automatic perturbation topology builder based on a graph-matching algorithm is developed, which can identify the maximum common substructure (MCS) of two or multiple molecules and provide the perturbation topologies suitable for free-energy calculations using the GROMOS and the GROMACS simulation packages. Various MCS search options are presented leading to alternative definitions of the perturbation pathway. Moreover, perturbation topologies generated using the default multistate MCS search are used to calculate the changes in free energy between lysine and its two post-translational modifications, 3-methyllysine and acetyllysine. The pairwise free-energy calculations performed on this test system led to a cycle closure of 0.5 ± 0.3 and 0.2 ± 0.2 kJ mol–1, with GROMOS and GROMACS simulation packages, respectively. The same relative free energies between the three states are obtained by employing the enveloping distribution sampling (EDS) approach when compared to the pairwise perturbations. Importantly, this toolkit is made available online as an open-source Python package (https://github.com/drazen-petrov/SMArt).

Local anesthetics transfer relies on pH differences and affinities toward lipophilic compartments

AbstractLocal anesthetics are weak bases and alter their properties in accordance with their protonation state, which depends on the environmental pH. We studied the transport dynamics of several local anesthetics from the extracellular fluid across biological membranes to the axoplasm, in order to understand the effect of pH on their pharmacodynamic properties. By using experimental pKa values and n‐octanol/water partition coefficients, we calculated pH‐dependent distribution coefficients and therewith associated relative populations and free energy profiles between extracellular fluid, membrane, and axoplasm. We estimated the local anesthetic capacity of neural tissue, using a simple 2D model. All values were calculated under physiological conditions and under the effect of local acidosis. Under physiological conditions, local anesthetics were most prevalent in the membrane. Change of free energy for transfer across biological membrane and the difference in relative population between extracellular fluid and axoplasm was dependent only on the difference of pH values between both environments, and not on pKa values. Estimated storage capacities for long‐lasting local anesthetics are higher by a factor of 10 or 100 than for short‐lasting local anesthetics. The membrane does not represent a barrier for local anesthetic diffusion. Local anesthetics move from the compartment with higher pH to the compartment with lower pH, due to favorable solvation free energy of protonated species. The rate of transfer across biological membrane is diffusion‐controlled and is similar for all local anesthetics. Neural tissue has a higher storage capacity for highly lipophilic local anesthetics, which can explain their longer duration of action.

Tuning the critical Li intercalation concentrations for MoX2 ( X=S , Se, or Te) bilayer phase transitions

Transition metal dichalcogenides, such as ${\mathrm{MoS}}_{2}$, are known to undergo a structural phase transformation as well as a change in the electronic conductivity upon Li intercalation. These properties make them candidates for charge-tunable ion-insertion materials that could be used in electrochemical devices. In this paper, we study the phase stability and electronic structure of H and T' Li-intercalated $\mathrm{Mo}{X}_{2}$ bilayers with X = S, Se, or Te. Using first-principles calculations in combination with classical and machine learning modeling approaches, we find that the H phase is more stable at low Li concentration for all X and the critical Li concentration at which the T'$\ensuremath{\rightarrow}\mathrm{H}$ transition occurs decreases with increasing mass of X. Furthermore, the relative free energy of the two phases becomes less sensitive to Li insertion with increasing atomic mass of the chalcogen atom X. While the electronic conductivity increases with increasing ion concentration at low concentrations, we do not observe a (positive) conductivity jump at the phase transition from H to T'.

Pyrrolizinone‐Fused BOPYINs: Characterization and Selective C‐O Bond Formation Mechanism

AbstractTwo novel pyrrolizinone‐fused BOPYINs (DAB−M and DAB−U) were prepared in one‐step through the oxidative and/or oxidative nucleophilic substitution of hydrogen (ONSH) reaction. Selective methoxyl substitution of α‐unsubstituted BOPYIN was developed, in which C−O and C=O bonds were formed and demonstrated by the single crystal structure of DAB−M. The absorption and emission spectra of both compounds showed red shift and large Stokes Shift compared with starting material DAB. Based on the TD‐DFT calculation, the electron transfer direction is totally different from DAB due to the carbonyl group on pyrrolizinone unit. Besides, a reasonable mechanism has been proposed and evidenced by quantum calculation in which the relative free energy has been exhibited.

S494 O-glycosylation site on the SARS-CoV-2 RBD affects the virus affinity to ACE2 and its infectivity; a molecular dynamics study

SARS-CoV-2 is a strain of Coronavirus family that caused the ongoing pandemic of COVID-19. Several studies showed that the glycosylation of virus spike (S) protein and the Angiotensin-Converting Enzyme 2 (ACE2) receptor on the host cell is critical for the virus infectivity. Molecular Dynamics (MD) simulations were used to explore the role of a novel mutated O-glycosylation site (D494S) on the Receptor Binding Domain (RBD) of S protein. This site was suggested as a key mediator of virus-host interaction. By exploring the dynamics of three O-glycosylated models and the control systems of unglcosylated S4944 and S494D complexes, it was shown that the decoration of S494 with elongated O-glycans results in stabilized interactions on the direct RBD-ACE2. Calculation of the distances between RBD and two major H1, H2 helices of ACE2 and the interacting pairs of amino acids in the interface showed that the elongated O-glycan maintains these interactions by forming several polar contacts with the neighbouring residues while it would not interfere in the direct binding interface. Relative binding free energy of RBD-ACE2 is also more favorable in the O-glycosylated models with longer glycans. The increase of RBD binding affinity to ACE2 depends on the size of attached O-glycan. By increasing the size of O-glycan, the RBD-ACE2 binding affinity will increase. Hence, this crucial factor must be taken into account for any further inhibitory approaches towards RBD-ACE2 interaction.

Quantifying Membrane Curvature Sensing of Peripheral Proteins by Simulated Buckling and Umbrella Sampling

Membrane curvature plays an essential role in the organization and trafficking of membrane associated proteins. Comparison or prediction of the experimentally resolved protein concentrations adopted at different membrane curvatures requires direct quantification of the relative partitioning free energy. Here, we present a highly efficient and simple to implement a free-energy calculation method which is able to directly resolve the relative partitioning free energy of proteins as a direct function of membrane curvature, i.e., a curvature sensing profile, within (coarse-grained) molecular dynamics simulations. We demonstrate its utility by resolving these profiles for two known curvature sensing peptides, namely ALPS and α-synuclein, for a membrane curvature ranging from -1/6.5 to +1/6.5 nm-1. We illustrate that the difference in relative partitioning (binding) free energy between these two extrema is only about 13 kBT for both peptides, illustrating that the driving force of curvature sensing is subtle. Furthermore, we illustrate that ALPS and α-synuclein sense curvature via a contrasting mechanism, which is differentially affected by membrane composition. In addition, we demonstrate that the intrinsic spontaneous curvature of both of these peptides lies beyond the range of membrane curvature accessible in micropipette aspiration experiments, being about 1/7 nm -1. Our approach offers an efficient and simple to implement in silico tool for exploring and screening the membrane curvature sensing mechanisms of proteins.