Free Energy Estimates Research Articles

Host-Guest Binding Free Energies à la Carte: An Automated OneOPES Protocol.

Estimating absolute binding free energies from molecular simulations is a key step in computer-aided drug design pipelines, but the agreement between computational results and experiments is still very inconsistent. Both the accuracy of the computational model and the quality of the statistical sampling contribute to this discrepancy, yet disentangling the two remains a challenge. In this study, we present an automated protocol based on OneOPES, an enhanced sampling method that exploits replica exchange and can accelerate several collective variables to address the sampling problem. We apply this protocol to 37 host-guest systems. The simplicity of setting up the simulations and producing well-converged binding free energy estimates without the need to optimize simulation parameters provides a reliable solution to the sampling problem. This, in turn, allows for a systematic force field comparison and ranking according to the correlation between simulations and experiments, which can inform the selection of an appropriate model. The protocol can be readily adapted to test more force field combinations and study more complex protein-ligand systems, where the choice of an appropriate physical model is often based on heuristic considerations rather than systematic optimization.

Molecular mechanism underlying effect of D93 and D289 protonation states on inhibitor-BACE1 binding: exploration from multiple independent Gaussian accelerated molecular dynamics and deep learning

ABSTRACT BACE1 has been regarded as an essential drug design target for treating Alzheimer’s disease (AD). Multiple independent Gaussian accelerated molecular dynamics simulations (GaMD), deep learning (DL), and molecular mechanics general Born surface area (MM-GBSA) method are integrated to elucidate the molecular mechanism underlying the effect of D93 and D289 protonation on binding of inhibitors OV6 and 4B2 to BACE1. The GaMD trajectory-based DL successfully identifies significant function domains. Dynamic analysis shows that the protonation of D93 and D289 strongly affects the structural flexibility and dynamic behaviour of BACE1. Free energy landscapes indicate that inhibitor-bound BACE1s have more conformational states in the protonated states than the wild-type (WT) BACE1, and show more binding poses of inhibitors. Binding affinities calculated using the MM-GBSA method indicate that the protonation of D93 and D289 highly disturbs the binding ability of inhibitors to BACE1. In addition, the protonation of two residues significantly affects the hydrogen bonding interactions (HBIs) of OV6 and 4B2 with BACE1, altering their binding activity to BACE1. The binding hot spots of BACE1 recognized by residue-based free energy estimations provide rational targeting sites for drug design towards BACE1. This study is anticipated to provide theoretical aids for drug development towards treatment of AD.

Pharmacophore-guided in-silico discovery of SIRT1 inhibitors for targeted cancer therapy

Epigenetic modifier, Sirtuin (SIRTs) is a family of seven isoforms (SIRT1-7) and nicotinamide adenine dinucleotide (NAD+) dependent class III histone deacetylase (HDACs) protein. SIRT1 in association with the p53 protein can regulate crucial cell processes such as glucose metabolism, lipid metabolism, mitochondrial biogenesis, DNA repair, oxidative stress, apoptosis, and inflammation through the process of deacetylation. When SIRT1 deacetylates p53, it loses its tumor suppression property. To promote apoptosis and decrease cell proliferation by inhibiting SIRT1 protein and ultimately raising the acetylation of p53 to regain its tumor suppressor function. Though we have many SIRT1 protein inhibitors, they exhibited off-target effects and inefficiency at the clinical trial stage. This study has been executed to identify more potentially effective and reliable SIRT1 inhibitors that can perform better than the existing options. To do so, pharmacophore-based screening of compound libraries followed by virtual screening, pharmacokinetic, drug-likeness, and toxicity studies were conducted which gave 42 compounds to evaluate further. Subsequently, exhaustive molecular docking and molecular dynamics simulation predicted four potential hits to inhibit the SIRT1 protein better than the reference compound. Further studies such as principal components analysis, free energy landscape, and estimation of binding free energy were done which concluded Hit4 (PubChem ID: 55753455) to be a novel and potent SIRT1 small molecule inhibitor among the others. The total binding free energy for Hit4 was found to be -44.68kcal/mol much better than the reference complex i.e., -29.38kcal/mol.

In Vitro and In Silico Assessment of Bioactivity Potencies With Components of Aristolochia bodamae Dingler Extracts From Turkey

AbstractAristolochia species have been widely used in folk medicine. In this work, six solvent extracts of Aristoloia bodamae leaf, seed, and flower were investigated for the photochemical contents (TPC, TFC, and LC–MS/MS), bioactivities. Additionally, molecular docking was applied to identify whether the most abundant compounds in the extracts are responsible for the bioactivity. Total phenol and flavonoid content ranges were 10.32–160.54 mg GAE/g and 3.42–50.03 mg QE/g, respectively. The most common chemical found in most extracts was o‐coumaric acid. Leaf extracts showed the highest total antioxidant, reducing power, and free radical removal activities. Seed extracts recorded the highest antibacterial activity, and for the inhibition activity seed extracts were also the highest in general. Molecular docking demonstrates firm binding between o‐coumaric acid and AChE. The stability of the best‐docked protein was evaluated using 100 ns molecular dynamic simulation and an MM‐PBSA‐assisted effective free energy estimation. The results showed that the interaction between this enzyme and the compound was remarkably stable according to energy and molecular dynamics calculations. Overall, aboveground extracts of A. bodamae exhibited high potential in reducing oxidative damage, enzyme inhibition, DNA protection potential, and antibacterial activities, probably due to their phenolic components and o‐coumaric acid.

Binding Selectivity Analysis from Alchemical Receptor Hopping and Swapping Free Energy Calculations.

We present receptor hopping and receptor swapping free energy estimation protocols based on the Alchemical Transfer Method (ATM) to model the binding selectivity of a set of ligands to two arbitrary receptors. The receptor hopping protocol, where a ligand is alchemically transferred from one receptor to another in one simulation, directly yields the ligand's binding selectivity free energy (BSFE) for the two receptors, which is the difference between the two individual binding free energies. In the receptor swapping protocol, the first ligand of a pair is transferred from one receptor to another while the second ligand is simultaneously transferred in the opposite direction. The receptor swapping free energy yields the differences in binding selectivity free energies of a set of ligands, which, when combined using a generalized DiffNet algorithm, yield the binding selectivity free energies of the ligands. We test these algorithms on host-guest systems and show that they yield results that agree with experimental data and are consistent with differences in absolute and relative binding free energies obtained by conventional methods. Preliminary applications to the selectivity analysis of molecular fragments binding to the trypsin and thrombin serine protease confirm the potential of the receptor swapping technology in structure-based drug discovery. The novel methodologies presented in this work are a first step toward streamlined and computationally efficient protocols for ligand selectivity optimization between mutants and homologous proteins.

Stretchable conducting polymer PEDOT:PSS treated with hard‐cation‐soft‐anion ionic liquid designed from molecular modeling

AbstractPEDOT:PSS, an ionic polymer mixture of positively‐charged poly‐3,4‐ethylenedioxythiophene (PEDOT+) and negatively‐charged poly‐styrenesulfonate (PSS−), is a water‐processable and environmentally‐benign organic semiconductor and electrochemical transistor, which plays a key role in organic (bio)electronic devices. However, pristine PEDOT:PSS films form 10‐to‐30‐nm granular domains, where conducting‐but‐hydrophobic PEDOT‐rich cores are surrounded by hydrophilic‐but‐insulating PSS‐rich shells. Such morphology makes PEDOT:PSS water‐soluble and thermally stable but very poor in conductivity. A tremendous amount of effort has been made to enhance the conductivity of PEDOT:PSS by restoring the extended conduction network of PEDOT. Recently, remarkable ~5000‐fold improvements of conductivity have been achieved by mixing PEDOT:PSS with proper ionic liquids (ILs). In a series of free energy estimations using density functional theory calculation and molecular dynamics simulation, we have demonstrated that the classic hard‐soft acid–base (or cation‐anion) principle of chemistry plays an important role in such improvements. Ion exchange between PEDOT+:PSS− and A+:X− ILs helps PEDOT+ to decouple from PSS− and to grow into large‐scale conducting domains of π‐stacked PEDOT+ decorated by IL anions X−. Thus, the most spontaneous decoupling between soft (hydrophobic) PEDOT+ and hard (hydrophilic) PSS− would be induced by strong interaction with soft anions X− and hard cations A+, respectively. Such hard‐cation‐soft‐anion principles have led us to design ILs containing extremely hydrophilic (i.e., protic) cations and hydrophobic anions. Not only they indeed improve the conductivity of PEDOT:PSS but also enhance its stretchability as well. In summary, our modeling offered molecular‐level insights on the morphological, electrical, and mechanical properties of PEDOT:PSS and a molecular‐interaction‐based enhancement strategy for intrinsically stretchable conductive polymers.

Accurate protein-ligand binding free energy estimation using QM/MM on multi-conformers predicted from classical mining minima

Accurate prediction of binding free energy is crucial for the rational design of drug candidates and understanding protein-ligand interactions. To address this, we have developed four protocols that combine QM/MM calculations and the mining minima (M2) method, tested on 9 targets and 203 ligands. Our protocols carry out free energy processing with or without conformational search on the selected conformers obtained from M2 calculations, where their force field atomic charge parameters are substituted with those obtained from a QM/MM calculation. The method achieved a high Pearson’s correlation coefficient (0.81) with experimental binding free energies across diverse targets, demonstrating its generality. Using a differential evolution algorithm with a universal scaling factor of 0.2, we achieved a low mean absolute error of 0.60 kcal mol-1. This performance surpasses many existing methods and is comparable to popular relative binding free energy techniques but at significantly lower computational cost.

Estimation of Bond Free Energy with Gmx_Mmpbsa in Ndm-1 Complexes

We report the values obtained for the binding free energies (ΔGbind) of the complexes: NDM-1-M25 (43.80 kcal/mol), NDM-1-M26 (12.71 kcal/mol), NDM-1-M35 (19.92 kcal/mol), NDM-1-M37 (2.46 kcal/mol) and the reference system NDM-1-Meropenem (-10.09 kcal/mol). These results are based on previous absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties, docking, and molecular dynamics calculations performed on a small library of potential NDM-1 enzyme inhibitors reported in the literature. Subsequently, the selected systems were evaluated by the Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA) method using the gmx_MMPBSA software. The MMPBSA method has been widely adopted to estimate protein-ligand binding affinities due to its efficiency and high correlation with experimental data. The obtained results suggest that, unlike the reference complex, the studied ligands probably do not bind to the receptor under the simulation conditions, which is in significant agreement with experimental data.

Efficient mapping of phase diagrams with conditional Boltzmann Generators

Abstract The accurate prediction of phase diagrams is of central importance for both the fundamental understanding of materials as well as for technological applications in material sciences. However, the computational prediction of the relative stability between phases based on their free energy is a daunting task, as traditional free energy estimators require a large amount of simulation data to obtain uncorrelated equilibrium samples over a grid of thermodynamic states. In this work, we develop deep generative machine learning models based on the Boltzmann Generator approach for entire phase diagrams, employing normalizing flows conditioned on the thermodynamic states, e.g., temperature and pressure, that they map to. By training a single normalizing flow to transform the equilibrium distribution sampled at only one reference thermodynamic state to a wide range of target temperatures and pressures, we can efficiently generate equilibrium samples across the entire phase diagram. Using a permutation-equivariant architecture allows us, thereby, to treat solid and liquid phases on the same footing. We demonstrate our approach by predicting the solid-liquid coexistence line for a Lennard-Jones system in excellent agreement with state-of-the-art free energy methods while significantly reducing the number of energy evaluations needed.

Amalgamated Pharmacoinformatics Study to Investigate the Mechanism of Xiao Jianzhong Tang against Chronic Atrophic Gastritis.

Traditional Chinese medicine (TCM) Xiao Jianzhong Tang (XJZ) has a favorable efficacy in the treatment of chronic atrophic gastritis (CAG). However, its pharmacological mechanism has not been fully explained. The purpose of this study was to find the potential mechanism of XJZ in the treatment of CAG using pharmacocoinformatics approaches. Network pharmacology was used to screen out the key compounds and key targets, MODELLER and GNNRefine were used to repair and refine proteins, Autodock vina was employed to perform molecular docking, Δ Lin_F9XGB was used to score the docking results, and Gromacs was used to perform molecular dynamics simulations (MD). Kaempferol, licochalcone A, and naringenin, were obtained as key compounds, while AKT1, MAPK1, MAPK14, RELA, STAT1, and STAT3 were acquired as key targets. Among docking results, 12 complexes scored greater than five. They were run for 50ns MD. The free binding energy of AKT1-licochalcone A and MAPK1-licochalcone A was less than -15 kcal/mol and AKT1-naringenin and STAT3-licochalcone A was less than -9 kcal/mol. These complexes were crucial in XJZ treating CAG. Our findings suggest that licochalcone A could act on AKT1, MAPK1, and STAT3, and naringenin could act on AKT1 to play the potential therapeutic effect on CAG. The work also provides a powerful approach to interpreting the complex mechanism of TCM through the amalgamation of network pharmacology, deep learning-based protein refinement, molecular docking, machine learning-based binding affinity estimation, MD simulations, and MM-PBSA-based estimation of binding free energy.

Leveraging neural networks to correct FoldX free energy estimates.

Proteins play a pivotal role in many biological processes, and changes in their amino acid sequences can lead to dysfunction and disease. These changes can affect protein folding or interaction with other biomolecules, such as preventing antibodies from inhibiting a viral infection or causing proteins to misfold. The ability to predict the effects of mutations in proteins is crucial. Although experimental techniques can accurately quantify the effect of mutations on protein folding free energies and protein-protein binding free energies, they are often time-consuming and costly. By contrast, computational techniques offer fast and cost-effective alternatives for estimating free energies, but they typically suffer from lower accuracy. Enhancing the accuracy of computational predictions is therefore of high importance, with the potential to greatly impact fields ranging from drug design to understanding disease mechanisms. One such widely used computational method, FoldX, is capable of rapidly predicting the relative folding stability ( ) for a protein as well as the relative binding affinity ( ) between proteins using a single protein structure as input. However, it can suffer from low accuracy, especially for antibody-antigen systems. In this work, we trained a neural network on FoldX output to enhance its prediction accuracy. We first performed FoldX calculations on the largest datasets available for mutations that affect binding (SKEMPIv2) and folding (ProTherm4) with experimentally measured . Features were then extracted from the FoldX output files including its prediction for . We then developed and optimized a neural network framework to predict the difference between FoldX's estimated and the experimental data, creating a model capable of producing a correction factor. Our approach showed significant improvements in Pearson correlation performance. For single mutations affecting folding, the correlation improved from a baseline of 0.3 to 0.66. In terms of binding, performance increased from 0.37 to 0.61 for single mutations and from 0.52 to 0.81 for double mutations. For epistasis, the correlation for binding affinity (both singles and doubles) improved from 0.19 to 0.59. Our results also indicated that models trained on double mutations enhanced accuracy when predicting higher-order mutations (such as triple or quadruple mutations), whereas models trained on singles did not. This suggests that interaction energy and epistasis effects present in the FoldX output are not fully utilized by FoldX itself. Once trained, these models add minimal computational time but provide a substantial increase in performance, especially for higher-order mutations and epistasis. This makes them a valuable addition to any free energy prediction pipeline using FoldX. Furthermore, we believe this technique can be further optimized and tested for predicting antibody escape, aiding in the efficient development of watch lists.

Cheminformatics-enhanced discovery of therapeutic agents targeting isocitrate lyase in Mycobacterium tuberculosis infections

Tuberculosis (TB) is a global health challenge; therefore, there is an urgent requirement to develop a novel and more effective anti-TB therapeutic. This study targeted the isocitrate lyase (ICL) protein due to its pivotal role in the pathogenicity of Mycobacterium tuberculosis (Mtb). Virtual screening of 8752 bioactive compounds used an ML-based QSAR model and molecular docking. ADMET testing was performed on the top three hits to identify the compound most closely mimicking a drug molecule. The top hits, 648 and 2785758, showed high binding affinity towards ICL with −7.3 and −7 kcal/mol, comparable to the control. These molecules also showed strong binding with the residue Asp108, which plays a vital role in ICL activity. Molecular dynamics simulations showed stability for 648 and 2785758, comparable to the control compound used in this study. It was found that 648 bound to the protein maintained the RMSD constant and consistent at 0.3 nm for a complete 100 ns simulation. 2785758 showed a comparable RMSD trend to the control. Both 648 and 2785758 showed high RMSF for critical residue Asp108. Further, PCA and FEL confirmed the formation of a stable complex. MM/GBSA estimations of binding free energy indicated that compounds 648 had an elevated level of stability (ΔG TOTAL = −28.11 kcal/mol) and 2785758 (ΔG TOTAL = −21.05 kcal/mol). This study suggests that compounds 648 and 2785758 can potentially affect the activity of ICL, leading to its inactivation and ultimately preventing the progression of tuberculosis. Communicated by Ramaswamy H. Sarma

Automated Adaptive Absolute Binding Free Energy Calculations.

Alchemical absolute binding free energy (ABFE) calculations have substantial potential in drug discovery, but are often prohibitively computationally expensive. To unlock their potential, efficient automated ABFE workflows are required to reduce both computational cost and human intervention. We present a fully automated ABFE workflow based on the automated selection of λ windows, the ensemble-based detection of equilibration, and the adaptive allocation of sampling time based on inter-replicate statistics. We find that the automated selection of intermediate states with consistent overlap is rapid, robust, and simple to implement. Robust detection of equilibration is achieved with a paired t-test between the free energy estimates at initial and final portions of a an ensemble of runs. We determine reasonable default parameters for all algorithms and show that the full workflow produces equivalent results to a nonadaptive scheme over a variety of test systems, while often accelerating equilibration. Our complete workflow is implemented in the open-source package A3FE (https://github.com/michellab/a3fe).

Exploring the Effects of Chirality of 5-methyl-5-[4-(4-oxo-3H-quinazolin-2-yl) phenyl]imidazolidine-2,4-dione and its Derivatives on the Oncological Target Tankyrase 2. Atomistic Insights.

Tankyrases (TNKS) are homomultimers existing in two forms, viz. TNKS1 and TNKS2. TNKS2 plays a pivotal role in carcinogenesis by activating the Wnt//β-catenin pathway. TNKS2 has been identified as a suitable target in oncology due to its crucial role in mediating tumour progression. The discovery of 5-methyl-5-[4-(4-oxo-3H-quinazolin-2-yl) phenyl] imidazolidine-2,4-dione, a hydantoin phenylquinazolinone derivative which exists as a racemic mixture and in its pure enantiomer forms, has reportedly exhibited inhibitory potency towards TNKS2. However, the molecular events surrounding its chirality towards TNKS2 remain unresolved. Herein, we employed in silico methods such as molecular dynamics simulation coupled with binding free energy estimations to explore the mechanistic activity of the racemic inhibitor and its enantiomer forms on TNK2 at a molecular level Results: Favourable binding free energies were noted for all three ligands propelled by electrostatic and van der Waals forces. The positive enantiomer demonstrated the highest total binding free energy (-38.15 kcal/mol), exhibiting a more potent binding affinity to TNKS2. Amino acids PHE1035, ALA1038, and HIS1048; PHE1035, HIS1048 and ILE1039; and TYR1060, SER1033 and ILE1059 were identified as key drivers of TNKS2 inhibition for all three inhibitors, characterized by the contribution of highest residual energies and the formation of crucial high-affinity interactions with the bound inhibitors. Further assessment of chirality by the inhibitors revealed a stabilizing effect of the complex systems of all three inhibitors on the TNKS2 structure. Concerning flexibility and mobility, the racemic inhibitor and negative enantiomer revealed a more rigid structure when bound to TNKS2, which could potentiate biological activity interference. The positive enantiomer, however, displayed much more elasticity and flexibility when bound to TNKS2. Overall, 5-methyl-5-[4-(4-oxo-3H-quinazolin-2-yl) phenyl] imidazolidine-2,4-dione and its derivatives showed their inhibitory prowess when bound to the TNKS2 target via in silico assessment. Thus, results from this study offer insight into chirality and the possibility of adjustments of the enantiomer ratio to promote greater inhibitory results. These results could also offer insight into lead optimization to enhance inhibitory effects.

Time-asymmetric fluctuation theorem and efficient free-energy estimation.

The free-energy difference ΔF between two high-dimensional systems is notoriously difficult to compute but very important for many applications such as drug discovery. We demonstrate that an unconventional definition of work introduced by Vaikuntanathan and Jarzynski (2008) satisfies a microscopic fluctuation theorem that relates path ensembles that are driven by protocols unequal under time reversal. It has been shown before that counterdiabatic protocols-those having additional forcing that enforces the system to remain in instantaneous equilibrium, also known as escorted dynamics or engineered swift equilibration-yield zero-variance work measurements for this definition. We show that this time-asymmetric microscopic fluctuation theorem can be exploited for efficient free-energy estimation by developing a simple (i.e., neural-network free) and efficient adaptive time-asymmetric protocol optimization algorithm that yields ΔF estimates that are orders of magnitude lower in mean squared error than the generic linear interpolation protocol with which it is initialized.

Metadynamics Study of Lipid-Mediated Antibacterial Toxin Binding to the EmrE Multiefflux Protein.

EmrE is a bacterial efflux protein in the small multidrug-resistant (SMR) family present in Escherichia coli. Due to its small size, 110 residues in each dimer subunit, it is an ideal model system to study ligand-protein-membrane interactions. Here in our work, we have calculated the free energy landscape of benzyltrimetylammonium (BTMA) and tetraphenyl phosphonium (TPP) binding to EmrE using the enhanced sampling method-multiple walker metadynamics. We estimate that the free energy of BTMA binding to EmrE is -21.2 ± 3.3 kJ/mol and for TPP is -43.6 ± 3.8 kJ/mol. BTMA passes through two metastable states to reach the binding pocket, while TPP has a more complex binding landscape with four metastable states and one main binding site. Our simulations show that the ligands interact with the membrane lipids at a distance 1 nm away from the binding site which forms a broad local minimum, consistent for both BTMA and TPP. This site can be an alternate entry point for ligands to partition from the membrane into the protein, especially for bulky and/or branched ligands. We also observed the membrane lipid and C-terminal 110HisA form salt-bridge interactions with the helix-1 residue 22LysB. Our free energy estimates and clusters are in close agreement with experimental data and give us an atomistic view of the ligand-protein-lipid interactions. Understanding the binding pathway of these ligands can guide us in future design of ligands that can alter or halt the function of EmrE.

Druglike Molecules Binding to Large Membrane Proteins: Absolute Binding Free Energy Computation.

In this research, we employed the alchemical double-decoupling method alongside restraining potentials, coupled with the FEPMD method, to ascertain the standard binding free energy of a drug-like molecule termed BHQ and three analogous compounds engineered with progressive addition of bulky para-alkyl groups binding to SERCA (Ca2+-ATPase of skeletal muscle sarcoplasmic reticulum). Integral transmembrane proteins represent crucial drug targets in numerous therapeutic interventions, presenting computational challenges due to their considerable system sizes. Our approach integrated the generalized born potential method and the spherical solvent boundary potential method, allowing us to explicitly focus on the active binding site while treating the remainder of the system implicitly. We evaluated contributions to the standard binding free energy from distinct interaction potentials: electrostatic, repulsive, dispersive, and restraining potentials, computed separately. The resulting absolute binding free energy of BHQ (11.63 kcal/mol) closely aligns with experimental measurements (10.56 kcal/mol). Notably, an accurate estimation of the absolute binding free energy was achieved for the simplest analog, created with the addition of a single para-methyl group. However, the analog with two para-methyl groups exhibited the highest binding free energy, which disagreed with experimental results. Determining the binding free energy of the BHQ analog engineered with three para-methyl groups presented challenges in convergence and resulted in the lowest free energy among the three.

Unveiling the Potential of Bergenia Phenolics: Vitexin's Role in Allosteric Modulation of PBP2a as a Strategy against MRSA Resistance.

For cell wall biosynthesis, drug-resistant S. aureus uses a special protein called PBP2a, even when antibiotics are present and stop its natural processes from working. To combat this, novel therapies are required to specifically target PBP2a with greater efficacy. Using computational approaches, we screened nine phenolic compounds from other Bergenia species, including Bergenia ciliata, Begenia ligulata, Bergenia purpurascens, and Ber-genia stracheyi, against the PBP2a allosteric site to explore the potential interaction between phe-nolic compounds and a specific region of PBP2a known as the allosteric site. Based on interaction patterns and estimated affinity, vitexin has been found to be the most prominent phenolic compound. We performed MD simulations on vitexin and ceftazidime as control molecules based on the docking results. The binding free energy estimates of vitexin (-94.48 +/- 17.92 kJ/mol) using MM/PBSA were lower than those of the control (-67.61 +/- 12.29 kJ/mol), which suggests that vitexin may be able to inhibit PBP2a activity in MRSA. It has been intriguing to observe a correlation between the affinity of the lead com-pounds at the allosteric site and the modification of Tyr446, the active site gatekeeper residue in PBP2a. Our findings have implied that lead compounds can either directly or indirectly decrease PBP2a activity by inducing allosteric site change in conventional medicine.

Variational time reversal for free-energy estimation in nonequilibrium steady states.

Studying the structure of systems in nonequilibrium steady states necessitates tools that quantify population shifts and associated deformations of equilibrium free-energy landscapes under persistent currents. Within the framework of stochastic thermodynamics, we establish a variant of the Kawasaki-Crooks equality that relates nonequilibrium free-energy corrections in overdamped Langevin systems to heat dissipation statistics along time-reversed relaxation trajectories computable with molecular simulation. Using stochastic control theory, we arrive at a general variational approach to evaluate the Kawasaki-Crooks equality and use it to estimate distribution functions of order parameters in specific models of driven and active matter, attaining substantial improvement in accuracy over simple perturbative methods.

Molecular docking, MD simulation, and MMGBSA-binding free energy estimation study identify antibiotic analogs as potential antimalarials targeting housekeeping proteins of Plasmodium falciparum apicoplast

ABSTRACT Plasmodium falciparum exclusively harbours a non-photosynthetic plastid-like organelle called ‘apicoplast,’ crucial for survival. The apicoplast genome resembles chloroplasts, albeit much reduced at 35 kb. The housekeeping functions of the apicoplast are similar to prokaryotes, and several antibiotics have been reported to kill the malaria parasite by targeting its housekeeping functions. However, a significant structural difference between bacterial and apicoplast housekeeping proteins promotes the discovery of antibiotic analogs specifically targeting apicoplast proteins. In this study, we used a high-throughput virtual screening approach to analyze 80% of structurally similar antibiotic analog libraries against five protein targets, including histone-like protein (PfHU), gyrase A subunit (PfGyrA), RNA polymerase β subunit (PfRpoβ), elongation factor G (PfEF-G), and elongation factor Tu (PfEF-Tu) involved in different housekeeping functions of the apicoplast. This computational screening identified molecules with better binding affinities than those antibiotics shown to have antimalarial activities, suggesting they may be more potent antimalarial compounds. The molecular dynamics simulations study revealed stable conformational changes with strong hydrogen bonds between the protein–ligand complexes. The top-hit antibiotic analogs exhibit favourable drug-like characteristics, making them promising candidates for future clinical studies. We propose further biochemical and phenotypic testing of these antibiotic analogs to develop more effective apicoplast-targeting antimalarials.