Predicted Binding Free Energies Research Articles

Improving the Performance of Screening MM/PBSA in Protein-Ligand Interactions via Machine Learning

Abstract Accurately estimating protein-ligand binding free energy is crucial for drug design and biophysics, yet remains a challenging task. In this study, we applied the screening molecular mechanics/Poisson Boltzmann surface area (MM/PBSA) method in combination with various machine learning techniques to compute the binding free energies of protein-ligand interactions. Our results demonstrate that machine learning outperforms direct screening MM/PBSA calculations in predicting protein-ligand binding free energies. Notably, the random forest (RF) method exhibited the best predictive performance, with a Pearson correlation coefficient (r p ) of 0.702 and a mean absolute error (MAE) of 1.379 kcal/mol. Furthermore, we analyzed feature importance rankings in the gradient boosting (GB), adaptive boosting (AdaBoost), and RF methods, and found that feature selection significantly impacted predictive performance. In particular, molecular weight (MW) and van der Waals (VDW) energies played a decisive role in the prediction. Overall, this study highlights the potential of combining machine learning methods with screening MM/PBSA for accurately predicting binding free energies in biosystems.

A head-to-head comparison of MM/PBSA and MM/GBSA in predicting binding affinities for the CB1 cannabinoid ligands.

The substantial increase in the number of active and inactive-state CB1 receptor experimental structures has provided opportunities for CB1 drug discovery using various structure-based drug design methods, including the popular end-point methods for predicting binding free energies-Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) and Molecular Mechanics/Generalized Born Surface Area (MM/GBSA). In this study, we have therefore evaluated the performance of MM/PBSA and MM/GBSA in calculating binding free energies for CB1 receptor. Additionally, with both MM/PBSA and MM/GBSA being known for their highly individualized performance, we have evaluated the effects of various simulation parameters including the use of energy minimized structures, choice of solute dielectric constant, inclusion of entropy, and the effects of the five GB models. Generally, MM/GBSA provided higher correlations than MM/PBSA (rMM/GBSA = 0.433 - 0.652 vs. rMM/PBSA = 0.100 - 0.486) regardless of the simulation parameters, while also offering faster calculations. Improved correlations were observed with the use of molecular dynamics ensembles compared with energyminimized structures and larger solute dielectric constants. Incorporation of entropic terms led to unfavorable results for both MM/PBSA and MM/GBSA for a majority of the dataset, while the evaluation of the various GB models exerted a varying effect on both the datasets. The findings obtained in this study demonstrate the utility of MM/PBSA and MM/GBSA in predicting binding free energies for the CB1 receptor, hence providing a useful benchmark for their applicability in the endocannabinoid system as well as other G protein-coupled receptors. The study utilized the docked dataset (Induced Fit Docking with Glide XP scoring function) from Loo et al., consisting of 46 ligands-23 agonists and 23 antagonists. The equilibrated structures from Loo et al. were subjected to 30ns production simulations using GROMACS 2018 at 300K and 1atm with the velocity rescaling thermostat and the Parinello-Rahman barostat. AMBER ff99SB*-ILDN was used for the proteins, General Amber Force Field (GAFF) was used for the ligands, and Slipids parameters were used for lipids. MM/PBSA and MM/GBSA binding free energies were then calculated using gmx_MMPBSA. The solute dielectric constant was varied between 1, 2, and 4 to study the effect of different solute dielectric constants on the performance of MM/PB(GB)SA. The effect of entropy on MM/PB(GB)SA binding free energies was evaluated using the interaction entropy module implemented in gmx_MMPBSA. Five GB models, GBHCT, GBOBC1, GBOBC2, GBNeck, and GBNeck2, were evaluated to study the effect of the choice of GB models in the performance of MM/GBSA. Pearson correlation coefficients were used to measure the correlation between experimental and predicted binding free energies.

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.

Engineering affinity of humanized ScFv targeting CD147 antibody: A combined approach of mCSM-AB2 and molecular dynamics simulations

This study aims to assess the effectiveness of mCSM-AB2, a graph-based signature machine learning method, for affinity engineering of the humanized single-chain Fv anti-CD147 (HuScFvM6-1B9). In parallel, molecular dynamics (MD) simulations were used to gain valuable insights into the dynamics and affinity of the HuScFvM6-1B9-CD147 complex. The result analysis involved integrating free energy changes calculated from the mCSM-AB2 with binding free energy predictions from MD simulations. The simulated structures of the modified HuScFvM6-1B9-CD147 domain 1 complex from MD simulations were used to highlight critical residues participating in the binding surface. Interestingly, alterations in the pattern of amino acids of HuScFvM6-1B9 at the complementarity determining regions interacting with the 31EDLGS35 epitope were observed, particularly in mutants that lost binding activity. The predicted mutants of HuScFvM6-1B9 were subsequently engineered and expressed in E. coli for subsequent binding property validation. Compared to WT HuScFvM6-1B9, the mutant HuScFvM6-1B9L1:N32Y exhibited a 1.66-fold increase in binding affinity, with a KD of 1.75 × 10−8 M. While mCSM-AB2 demonstrates insignificant improvement in predicting binding affinity enhancements, it excels at predicting negative effects, aligning well with experimental validation. In addition to binding free energies, total entropy was considered to explain the discrepancy between mCSM-AB2 predictions and experimental results. This study provides guidelines and identifies the limitations of mCSM-AB2 and MD simulations in antibody engineering.

SAAMBE-MEM: a sequence-based method for predicting binding free energy change upon mutation in membrane protein-protein complexes.

Mutations in protein-protein interactions can affect the corresponding complexes, impacting function and potentially leading to disease. Given the abundance of membrane proteins, it is crucial to assess the impact of mutations on the binding affinity of these proteins. Although several methods exist to predict the binding free energy change due to mutations in protein-protein complexes, most require structural information of the protein complex and are primarily trained on the SKEMPI database, which is composed mainly of soluble proteins. A novel sequence-based method (SAAMBE-MEM) for predicting binding free energy changes (ΔΔG) in membrane protein-protein complexes due to mutations has been developed. This method utilized the MPAD database, which contains binding affinities for wild-type and mutant membrane protein complexes. A machine learning model was developed to predict ΔΔG by leveraging features such as amino acid indices and position-specific scoring matrices (PSSM). Through extensive dataset curation and feature extraction, SAAMBE-MEM was trained and validated using the XGBoost regression algorithm. The optimal feature set, including PSSM-related features, achieved a Pearson correlation coefficient of 0.64, outperforming existing methods trained on the SKEMPI database. Furthermore, it was demonstrated that SAAMBE-MEM performs much better when utilizing evolution-based features in contrast to physicochemical features. The method is accessible via a web server and standalone code at http://compbio.clemson.edu/SAAMBE-MEM/. The cleaned MPAD database is available at the website.

Evaluation of AlphaFold 3's Protein-Protein Complexes for Predicting Binding Free Energy Changes upon Mutation.

AlphaFold 3 (AF3), the latest version of protein structure prediction software, goes beyond its predecessors by predicting protein-protein complexes. It could revolutionize drug discovery and protein engineering, marking a major step toward comprehensive, automated protein structure prediction. However, independent validation of AF3's predictions is necessary. In this work, we evaluate AF3 complex structures using the SKEMPI 2.0 database which involves 317 protein-protein complexes and 8338 mutations. AF3 complex structures when applied to the most advanced TDL model, MT-TopLap (MultiTask-Topological Laplacian), give rise to a very good Pearson correlation coefficient of 0.86 for predicting protein-protein binding free energy changes upon mutation, which is slightly less than the 0.88 achieved earlier with the Protein Data Bank (PDB) structures. Nonetheless, AF3 complex structures led to a 8.6% increase in the prediction RMSE compared to original PDB complex structures. Additionally, some of AF3's complex structures have large errors, which were not captured in its ipTM performance metric. Finally, it is found that AF3's complex structures are not reliable for intrinsically flexible regions or domains.

Novel anti-Acanthamoeba effects elicited by a repurposed poly (ADP-ribose) polymerase inhibitor AZ9482.

Acanthamoeba infection is a serious public health concern, necessitating the development of effective and safe anti-Acanthamoeba chemotherapies. Poly (ADP-ribose) polymerases (PARPs) govern a colossal amount of biological processes, such as DNA damage repair, protein degradation and apoptosis. Multiple PARP-targeted compounds have been approved for cancer treatment. However, repurposing of PARP inhibitors to treat Acanthamoeba is poorly understood. In the present study, we attempted to fill these knowledge gaps by performing anti-Acanthamoeba efficacy assays, cell biology experiments, bioinformatics, and transcriptomic analyses. Using a homology model of Acanthamoeba poly (ADP-ribose) polymerases (PARPs), molecular docking of approved drugs revealed three potential inhibitory compounds: olaparib, venadaparib and AZ9482. In particular, venadaparib exhibited superior docking scores (-13.71) and favorable predicted binding free energy (-89.28 kcal/mol), followed by AZ9482, which showed a docking score of -13.20 and a binding free energy of -92.13 kcal/mol. Notably, the positively charged cyclopropylamine in venadaparib established a salt bridge (through E535) and a hydrogen bond (via N531) within the binding pocket. For comparison, AZ9482 was well stacked by the surrounding aromatic residues including H625, Y652, Y659 and Y670. In an assessment of trophozoites viability, AZ9482 exhibited a dose-and time-dependent anti-trophozoite effect by suppressing Acanthamoeba PARP activity, unlike olaparib and venadaparib. An Annexin V-fluorescein isothiocyanate/propidium iodide apoptosis assay revealed AZ9482 induced trophozoite necrotic cell death rather than apoptosis. Transcriptomics analyses conducted on Acanthamoeba trophozoites treated with AZ9482 demonstrated an atlas of differentially regulated proteins and genes, and found that AZ9482 rapidly upregulates a multitude of DNA damage repair pathways in trophozoites, and intriguingly downregulates several virulent genes. Analyzing gene expression related to DNA damage repair pathway and the rate of apurinic/apyrimidinic (AP) sites indicated DNA damage efficacy and repair modulation in Acanthamoeba trophozoites following AZ9482 treatment. Collectively, these findings highlight AZ9482, as a structurally unique PARP inhibitor, provides a promising prototype for advancing anti-Acanthamoeba drug research.

Directional ΔG Neural Network (DrΔG-Net): A Modular Neural Network Approach to Binding Free Energy Prediction.

The protein-ligand binding free energy is a central quantity in structure-based computational drug discovery efforts. Although popular alchemical methods provide sound statistical means of computing the binding free energy of a large breadth of systems, they are generally too costly to be applied at the same frequency as end point or ligand-based methods. By contrast, these data-driven approaches are typically fast enough to address thousands of systems but with reduced transferability to unseen systems. We introduce DrΔG-Net (or simply Dragnet), an equivariant graph neural network that can blend ligand-based and protein-ligand data-driven approaches. It is based on a 3D fingerprint representation of the ligand alone and in complex with the protein target. Dragnet is a global scoring function to predict the binding affinity of arbitrary protein-ligand complexes, but can be easily tuned via transfer learning to specific systems or end points, performing similarly to common 2D ligand-based approaches in these tasks. Dragnet is evaluated on a total of 28 validation proteins with a set of congeneric ligands derived from the Binding DB and one custom set extracted from the ChEMBL Database. In general, a handful of experimental binding affinities are sufficient to optimize the scoring function for a particular protein and ligand scaffold. When not available, predictions from physics-based methods such as absolute free energy perturbation can be used for the transfer learning tuning of Dragnet. Furthermore, we use our data to illustrate the present limitations of data-driven modeling of binding free energy predictions.

PopShift: A Thermodynamically Sound Approach to Estimate Binding Free Energies by Accounting for Ligand-Induced Population Shifts from a Ligand-Free Markov State Model.

Obtaining accurate binding free energies from in silico screens has been a long-standing goal for the computational chemistry community. However, accuracy and computational cost are at odds with one another, limiting the utility of methods that perform this type of calculation. Many methods achieve massive scale by explicitly or implicitly assuming that the target protein adopts a single structure, or undergoes limited fluctuations around that structure, to minimize computational cost. Others simulate each protein-ligand complex of interest, accepting lower throughput in exchange for better predictions of binding affinities. Here, we present the PopShift framework for accounting for the ensemble of structures a protein adopts and their relative probabilities. Protein degrees of freedom are enumerated once, and then arbitrarily many molecules can be screened against this ensemble. Specifically, we use Markov state models (MSMs) as a compressed representation of a protein's thermodynamic ensemble. We start with a ligand-free MSM and then calculate how addition of a ligand shifts the populations of each protein conformational state based on the strength of the interaction between that protein conformation and the ligand. In this work we use docking to estimate the affinity between a given protein structure and ligand, but any estimator of binding affinities could be used in the PopShift framework. We test PopShift on the classic benchmark pocket T4 Lysozyme L99A. We find that PopShift is more accurate than common strategies, such as docking to a single structure and traditional ensemble docking─producing results that compare favorably with alchemical binding free energy calculations in terms of RMSE but not correlation─and may have a more favorable computational cost profile in some applications. In addition to predicting binding free energies and ligand poses, PopShift also provides insight into how the probability of different protein structures is shifted upon addition of various concentrations of ligand, providing a platform for predicting affinities and allosteric effects of ligand binding. Therefore, we expect PopShift will be valuable for hit finding and for providing insight into phenomena like allostery.

Binding Free Energy Calculation Based on the Fragment Molecular Orbital Method and Its Application in Designing Novel SHP-2 Allosteric Inhibitors.

The accurate prediction of binding free energy is a major challenge in structure-based drug design. Quantum mechanics (QM)-based approaches show promising potential in predicting ligand-protein binding affinity by accurately describing the behavior and structure of electrons. However, traditional QM calculations face computational limitations, hindering their practical application in drug design. Nevertheless, the fragment molecular orbital (FMO) method has gained widespread application in drug design due to its ability to reduce computational costs and achieve efficient ab initio QM calculations. Although the FMO method has demonstrated its reliability in calculating the gas phase potential energy, the binding of proteins and ligands also involves other contributing energy terms, such as solvent effects, the 'deformation energy' of a ligand's bioactive conformations, and entropy. Particularly in cases involving ionized fragments, the calculation of solvation free energy becomes particularly crucial. We conducted an evaluation of some previously reported implicit solvent methods on the same data set to assess their potential for improving the performance of the FMO method. Herein, we develop a new QM-based binding free energy calculation method called FMOScore, which enhances the performance of the FMO method. The FMOScore method incorporates linear fitting of various terms, including gas-phase potential energy, deformation energy, and solvation free energy. Compared to other widely used traditional prediction methods such as FEP+, MM/PBSA, MM/GBSA, and Autodock vina, FMOScore showed good performance in prediction accuracies. By constructing a retrospective case study, it was observed that incorporating calculations for solvation free energy and deformation energy can further enhance the precision of FMO predictions for binding affinity. Furthermore, using FMOScore-guided lead optimization against Src homology-2-containing protein tyrosine phosphatase 2 (SHP-2), we discovered a novel and potent allosteric SHP-2 inhibitor (compound 8).

Methodological and force field effects in the molecular dynamics-based prediction of binding free energies of host-guest systems.

As a contribution to the understanding and rationalization of methodological and modeling effects in recent host-guest SAMPL challenges, using an alchemical molecular dynamics technique we have examined the impact of force field parameterization and ionic strength in connection with guest charge neutralization on computed dissociation free energies in two typical SAMPL heavily charged macrocyclic hosts encapsulating small protonated amines with disparate binding affinities. We have shown that the methodological treatment for host neutralization, with explicit ions or with the background neutralizing plasma in the context of alchemical calculations under periodic boundary conditions, has a moderate effect on the calculated affinities. On the other hand, we have shown that seemingly small differences in the force field parameterization in highly symmetric hosts can produce systematic effects on the structural features that can have a significant impact on the predicted binding affinities.

Software for Predicting Binding Free Energy of Protein-Protein Complexes and Their Mutants.

Protein-protein binding affinity prediction is important for understanding complex biochemical pathways and to uncover protein interaction networks. Quantitative estimation of the binding affinity changes caused by mutations can provide critical information for protein function annotation and genetic disease diagnoses. The binding free energies of protein-protein complexes can be predicted using several computational tools. This chapter is a summary of software developed for the prediction of binding free energies for protein-protein complexes and their mutants.

Machine-learned molecular mechanics force fields from large-scale quantum chemical data.

The development of reliable and extensible molecular mechanics (MM) force fields-fast, empirical models characterizing the potential energy surface of molecular systems-is indispensable for biomolecular simulation and computer-aided drug design. Here, we introduce a generalized and extensible machine-learned MM force field, espaloma-0.3, and an end-to-end differentiable framework using graph neural networks to overcome the limitations of traditional rule-based methods. Trained in a single GPU-day to fit a large and diverse quantum chemical dataset of over 1.1 M energy and force calculations, espaloma-0.3 reproduces quantum chemical energetic properties of chemical domains highly relevant to drug discovery, including small molecules, peptides, and nucleic acids. Moreover, this force field maintains the quantum chemical energy-minimized geometries of small molecules and preserves the condensed phase properties of peptides and folded proteins, self-consistently parametrizing proteins and ligands to produce stable simulations leading to highly accurate predictions of binding free energies. This methodology demonstrates significant promise as a path forward for systematically building more accurate force fields that are easily extensible to new chemical domains of interest.

The SAMPL9 host-guest blind challenge: an overview of binding free energy predictive accuracy.

We report the results of the SAMPL9 host-guest blind challenge for predicting binding free energies. The challenge focused on macrocycles from pillar[n]-arene and cyclodextrin host families, including WP6, and bCD and HbCD. A variety of methods were used by participants to submit binding free energy predictions. A machine learning approach based on molecular descriptors achieved the highest accuracy (RMSE of 2.04 kcal mol-1) among the ranked methods in the WP6 dataset. Interestingly, predictions for WP6 obtained via docking tended to outperform all methods (RMSE of 1.70 kcal mol-1), most of which are MD based and computationally more expensive. In general, methods applying force fields achieved better correlation with experiments for WP6 opposed to the machine learning and docking models. In the cyclodextrin-phenothiazine challenge, the ATM approach emerged as the top performing method with RMSE less than 1.86 kcal mol-1. Correlation metrics of ranked methods in this dataset were relatively poor compared to WP6. We also highlight several lessons learned to guide future work and help improve studies on the systems discussed. For example, WP6 may be present in other microstates other than its -12 state in the presence of certain guests. Machine learning approaches can be used to fine tune or help train force fields for certain chemistry (i.e. WP6-G4). Certain phenothiazines occupy distinct primary and secondary orientations, some of which were considered individually for accurate binding free energies. The accuracy of predictions from certain methods while starting from a single binding pose/orientation demonstrates the sensitivity of calculated binding free energies to the orientation, and in some cases the likely dominant orientation for the system. Computational and experimental results suggest that guest phenothiazine core traverses both the secondary and primary faces of the cyclodextrin hosts, a bulky cationic side chain will primarily occupy the primary face, and the phenothiazine core substituent resides at the larger secondary face.

AAp-MSMD: Amino Acid Preference Mapping on Protein-Protein Interaction Surfaces Using Mixed-Solvent Molecular Dynamics.

Peptides have attracted much attention recently owing to their well-balanced properties as drugs against protein-protein interaction (PPI) surfaces. Molecular simulation-based predictions of binding sites and amino acid residues with high affinity to PPI surfaces are expected to accelerate the design of peptide drugs. Mixed-solvent molecular dynamics (MSMD), which adds probe molecules or fragments of functional groups as solutes to the hydration model, detects the binding hotspots and cryptic sites induced by small molecules. The detection results vary depending on the type of probe molecule; thus, they provide important information for drug design. For rational peptide drug design using MSMD, we proposed MSMD with amino acid residue probes, named amino acid probe-based MSMD (AAp-MSMD), to detect hotspots and identify favorable amino acid types on protein surfaces to which peptide drugs bind. We assessed our method in terms of hotspot detection at the amino acid probe level and binding free energy prediction with amino acid probes at the PPI site for the complex structure that formed the PPI. In hotspot detection, the max-spatial probability distribution map (max-PMAP) obtained from AAp-MSMD detected the PPI site, to which each type of amino acid can bind favorably. In the binding free energy prediction using amino acid probes, ΔGFE obtained from AAp-MSMD roughly estimated the experimental binding affinities from the structure-activity relationship. AAp-MSMD, with amino acid probes, provides estimated binding sites and favorable amino acid types at the PPI site of a target protein.

Theoretical study of the Cu2+-glycine interaction in ammonia and temperature effects

In this study, we have investigated the potential energy of surfaces of the various glycine tautomers, also the interaction between Cu2+ cation with these glycine tautomers in both the gas and solvent phases and for the first time, the glycine-Cu2+(NH3)n=3−6 clusters in the gas-phase. For the studied clusters, we reported their temperature-dependent conformer distributions, along with the binding energies per ammonia molecule and clustering energies. After comparing the binding energies obtained computationally by using the DLPNO-CCSD(T)/CBS level of theory to those obtained from ten DFT functionals (M06, M06HF, M06-2X, M08HX, MN15, MN12SX, ωB97XD, PW6B95D3, TPSSH, APFD), as well as from the ab initio MP2 method, we selected the DFT functional, M08HX, in conjunction with the aug-cc-pVDZ basis set, as our method of choice. Evidence emerged from our study that the solvation medium and the presence of the Cu2+ cation influence the stability of glycine tautomers. The relative stability of Gly-Cu2+(NH3)n clusters in the gas phase is influenced by the coordination of the Cu2+ cation. The study of the relative population in the gas phase of the Gly-Cu2+(NH3)n=3−6 clusters, predicts that for cluster size n=4, the structure of the more compact penta-coordinated Cu2+ ion dominates at low temperatures. Conversely, the conformers for the less compact tetra-coordinated Cu2+ cation are prevalent at high temperatures. For cluster size n=6 with more ammonia molecules per cluster, the conformers containing the compacted is penta-coordinated Cu2+ cation, are predominant at all temperatures. Binding energies computed for ionic interactions of the Gly-Cu2+ complexes, resulted in the following values at room temperature: the binding electronic energy (ΔEn), binding enthalpy (ΔHn) and binding free energy (ΔGn) of ionic interaction are respectively -218.3, -218.4, -211.6 kcal/mol in the gas phase and -93.8, -94.9, -85.2 kcal/mol in the solvent phase. In order to extrapolate this also to the Gly-Cu2+ ammonia clusters, a suitable function of best fit was applied. The computational prediction of binding electronic energy, binding enthalpy and binding free energy, per ammonia molecule in the gas phase, at saturation, produced the values -25.9, -26.1 and -17.0 kcal/mol, respectively. We therefore recommend this function fit for further study of such systems.

In silico analysis unravels the promising anticariogenic efficacy of fatty acids against dental caries causing Streptococcus mutans

Globally, dental caries is a prevalent oral disease caused by cariogenic bacteria, primarily Streptococcus mutans. It establishes caries either through sucrose-dependent (via glycosyltransferases) or through sucrose-independent (via surface adhesins Antigen I/II) mechanism. Sortase A (srtA) attaches virulence-associated adhesins to host tissues. Because of their importance in the formation of caries, targeting these proteins is decisive in the development of new anticariogenic drugs. High-throughput virtual screening with LIPID MAPS -a fatty acid database was performed. The selected protein-ligand complexes were subjected to molecular dynamics simulation (MDs). The Binding Free Energy of complexes was predicted using MM/PBSA. Further, the drug-likeness and pharmacokinetic properties of ligands were also analyzed. Out of 46,200 FAs scrutinized virtually against the three protein targets (viz., GtfC, Ag I/II and srtA), top 5 FAs for each protein were identified as the best hit based on interaction energies viz., hydrogen bond numbers and hydrophobic interaction. Further, two common FAs (LMFA01050418 and LMFA01040045) that showed high binding affinity against Ag I/II and srtA were selected for MDs analysis. A 100ns MDs unveiled a stable conformation. Results of Rg signified that FAs does not induce significant structural & conformational changes. SASA indicated that the complexes maintain higher thermodynamic stability during MDs. The predicted binding free energy (MM/PBSA) of complexes elucidated their stable binding interaction. ADME analysis suggested the FAs are biologically feasible as therapeutic candidates. Overall, the presented in silico data is the first of its kind in delineating FAs as promising anticaries agents of future. Communicated by Ramaswamy H. Sarma

Predicting the immune escape of SARS-CoV-2 neutralizing antibodies upon mutation

COVID-19 has resulted in millions of deaths and severe impact on economies worldwide. Moreover, the emergence of SARS-CoV-2 variants presented significant challenges in controlling the pandemic, particularly their potential to avoid the immune system and evade vaccine immunity. This has led to a growing need for research to predict how mutations in SARS-CoV-2 reduces the ability of antibodies to neutralize the virus. In this study, we assembled a set of 1813 mutations from the interface of SARS-CoV-2 spike protein's receptor binding domain (RBD) and neutralizing antibody complexes and developed a machine learning model to classify high or low escape mutations using interaction energy, inter-residue contacts and predicted binding free energy change. Our approach achieved an Area under the Receiver Operating Characteristics (ROC) Curve (AUC) of 0.91 using the Random Forest classifier on the test dataset with 217 mutations. The model was further utilized to predict the escape mutations on a dataset of 29,165 mutations located at the interface of 83 RBD-neutralizing antibody complexes. A small subset of this dataset was also validated based on available experimental data. We found that top 10 % high escape mutations were dominated by charged to nonpolar mutations whereas low escape mutations were dominated by polar to nonpolar mutations. We believe that the present method will allow prioritization of high/low escape mutations in the context of neutralizing antibodies targeting SARS-CoV-2 RBD region and assist antibody design for current and emerging variants.

The structure of a hibernating ribosome in a Lyme disease pathogen

The spirochete bacterial pathogen Borrelia (Borreliella) burgdorferi (Bbu) affects more than 10% of the world population and causes Lyme disease in about half a million people in the US annually. Therapy for Lyme disease includes antibiotics that target the Bbu ribosome. Here we present the structure of the Bbu 70S ribosome obtained by single particle cryo-electron microscopy at 2.9 Å resolution, revealing a bound hibernation promotion factor protein and two genetically non-annotated ribosomal proteins bS22 and bL38. The ribosomal protein uL30 in Bbu has an N-terminal α-helical extension, partly resembling the mycobacterial bL37 protein, suggesting evolution of bL37 and a shorter uL30 from a longer uL30 protein. Its analogy to proteins uL30m and mL63 in mammalian mitochondrial ribosomes also suggests a plausible evolutionary pathway for expansion of protein content in mammalian mitochondrial ribosomes. Computational binding free energy predictions for antibiotics reflect subtle distinctions in antibiotic-binding sites in the Bbu ribosome. Discovery of these features in the Bbu ribosome may enable better ribosome-targeted antibiotic design for Lyme disease treatment.

Comparison of Equilibrium and Nonequilibrium Approaches for Relative Binding Free Energy Predictions.

Alchemical relative binding free energy calculations have recently found important applications in drug optimization. A series of congeneric compounds are generated from a preidentified lead compound, and their relative binding affinities to a protein are assessed in order to optimize candidate drugs. While methods based on equilibrium thermodynamics have been extensively studied, an approach based on nonequilibrium methods has recently been reported together with claims of its superiority. However, these claims pay insufficient attention to the basis and reliability of both methods. Here we report a comparative study of the two approaches across a large data set, comprising more than 500 ligand transformations spanning in excess of 300 ligands binding to a set of 14 diverse protein targets. Ensemble methods are essential to quantify the uncertainty in these calculations, not only for the reasons already established in the equilibrium approach but also to ensure that the nonequilibrium calculations reside within their domain of validity. If and only if ensemble methods are applied, we find that the nonequilibrium method can achieve accuracy and precision comparable to those of the equilibrium approach. Compared to the equilibrium method, the nonequilibrium approach can reduce computational costs but introduces higher computational complexity and longer wall clock times. There are, however, cases where the standard length of a nonequilibrium transition is not sufficient, necessitating a complete rerun of the entire set of transitions. This significantly increases the computational cost and proves to be highly inconvenient during large-scale applications. Our findings provide a key set of recommendations that should be adopted for the reliable implementation of nonequilibrium approaches to relative binding free energy calculations in ligand-protein systems.