Predicted Binding Free Energies Research Articles

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

IntroductionAcanthamoeba 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.MethodsIn the present study, we attempted to fill these knowledge gaps by performing anti-Acanthamoeba efficacy assays, cell biology experiments, bioinformatics, and transcriptomic analyses.ResultsUsing 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.DiscussionCollectively, 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).

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.

Improving alkaline stability of α-l-rhamnosidase from Aspergillus niger through computational strategy combines with folding free energy and binding free energy

It is difficult to improve pH stability of enzymes. In the present study, a semi-rational strategy was validated to improve the alkaline stability of an α-L-rhamnosidase. The strategy combined the prediction of folding free energy and binding free energy. Through alanine scanning and virtual saturation mutation, 9 mutations were selected as mutant candidates. The mutant R307Y was confirmed with higher pH stability than the WT. In addition, the mutant also showed an increase of enzyme activity. The structure simulation and molecular dynamics analysis indicated that the interactions, especially hydrogen bonds, hydrophobic interactions and the local structural stability were enhanced by introducing aromatic residues, which play an important role in increasing the rigidity and maintaining the alkaline pH stability. The study not only provides a α-L-rhamnosidase mutant with better pH stability and enzyme activity, but also provides an effective strategy for improving enzyme pH stability.

Binding Mechanism of Inhibitors to Heat Shock Protein 90 Investigated by Multiple Independent Molecular Dynamics Simulations and Prediction of Binding Free Energy.

The heat shock protein (HSP90) has been an import target of drug design in the treatment of human disease. An exploration of the conformational changes in HSP90 can provide useful information for the development of efficient inhibitors targeting HSP90. In this work, multiple independent all-atom molecular dynamics (AAMD) simulations followed by calculations of the molecular mechanics generalized Born surface area (MM-GBSA) were performed to explore the binding mechanism of three inhibitors (W8Y, W8V, and W8S) to HSP90. The dynamics analyses verified that the presence of inhibitors impacts the structural flexibility, correlated movements, and dynamics behavior of HSP90. The results of the MM-GBSA calculations suggest that the selection of GB models and empirical parameters has important influences on the predicted results and verify that van der Waals interactions are the main forces that determine inhibitor-HSP90 binding. The contributions of separate residues to the inhibitor-HSP90 binding process indicate that hydrogen-bonding interactions (HBIs) and hydrophobic interactions play important roles in HSP90-inhibitor identifications. Moreover, residues L34, N37, D40, A41, D79, I82, G83, M84, F124, and T171 are recognized as hot spots of inhibitor-HSP90 binding and provide significant target sites of for the design of drugs related to HSP90. This study aims to contribute to the development of efficient inhibitors that target HSP90 by providing an energy-based and theoretical foundation.

Structural and energetic insights into the selective inhibition of PKMYT1 against WEE1

Protein kinase, membrane-associated tyrosine/threonine 1 (PKMYT1), a member of the WEE family and responsible for the regulation of CDK1 phosphorylation, has been considered a promising therapeutic target for cancer therapy. However, the highly structural conservation of the ATP-binding sites of the WEE family poses a challenge to the design of selective inhibitors for PKMYT1. Here, molecular docking, multiple microsecond-length molecular dynamics (MD) simulations and end-point free energy calculations were performed to uncover the molecular mechanism of the binding selectivity of RP-6306 toward PKMYT1 over its highly homologous kinase WEE1. The binding specificity of RP-6306 reported in previous experimental bioassays was clarified by MD simulations and binding free energy calculations. Further, the binding free energy prediction indicated that the binding selectivity of RP-6306 largely derived from the difference in the protein-ligand electrostatic interactions. The per-residue free energy decomposition suggested that the non-conserved gatekeeper residue in the hinge domain of PKMYT1/WEE1, Thr187/Asn376, is the critical factor responsible for the binding selectivity of RP-6306 toward PKMYT1. In addition, a water-mediated hydrogen bond was formed between RP-6306 and Gly191 at the hinge domain in the PKMYT1/RP-6306 complex, which was absent in the WEE1/RP-6306 complex. This study is expected to offer useful information for the design of more potent and selective PKMYT1 inhibitors. Communicated by Ramaswamy H. Sarma

Understanding the Structure-Activity Relationship through Density Functional Theory: A Simple Method Predicts Relative Binding Free Energies of Metalloenzyme Fragment-like Inhibitors.

Despite being involved in several human diseases, metalloenzymes are targeted by a small percentage of FDA-approved drugs. Development of novel and efficient inhibitors is required, as the chemical space of metal binding groups (MBGs) is currently limited to four main classes. The use of computational chemistry methods in drug discovery has gained momentum thanks to accurate estimates of binding modes and binding free energies of ligands to receptors. However, exact predictions of binding free energies in metalloenzymes are challenging due to the occurrence of nonclassical phenomena and interactions that common force field-based methods are unable to correctly describe. In this regard, we applied density functional theory (DFT) to predict the binding free energies and to understand the structure-activity relationship of metalloenzyme fragment-like inhibitors. We tested this method on a set of small-molecule inhibitors with different electronic properties and coordinating two Mn2+ ions in the binding site of the influenza RNA polymerase PAN endonuclease. We modeled the binding site using only atoms from the first coordination shell, hence reducing the computational cost. Thanks to the explicit treatment of electrons by DFT, we highlighted the main contributions to the binding free energies and the electronic features differentiating strong and weak inhibitors, achieving good qualitative correlation with the experimentally determined affinities. By introducing automated docking, we explored alternative ways to coordinate the metal centers and we identified 70% of the highest affinity inhibitors. This methodology provides a fast and predictive tool for the identification of key features of metalloenzyme MBGs, which can be useful for the design of new and efficient drugs targeting these ubiquitous proteins.

Quantitatively Accounting for Protein Reorganization in Computer-Aided Drug Design.

Structure-based drug design frequently operates under the assumption that a single holo structure is relevant. However, a large number of crystallographic examples clearly show that multiple conformations are possible. In those cases, the protein reorganization free energy must be known to accurately predict binding free energies for ligands. Only then can the energetic preference among these multiple protein conformations be utilized to design ligands with stronger binding potency and selectivity. Here, we present a computational method to quantify these protein reorganization free energies. We test it on two retrospective drug design cases, Abl kinase and HSP90, and illustrate how alternative holo conformations can be derisked and lead to large boosts in affinity. This method will allow computer-aided drug design to better support complex protein targets.

An optimized thermodynamics integration protocol for identifying beneficial mutations in antibody design.

Accurate identification of beneficial mutations is central to antibody design. Many knowledge-based (KB) computational approaches have been developed to predict beneficial mutations, but their accuracy leaves room for improvement. Thermodynamic integration (TI) is an alchemical free energy algorithm that offers an alternative technique for identifying beneficial mutations, but its performance has not been evaluated. In this study, we developed an efficient TI protocol with high accuracy for predicting binding free energy changes of antibody mutations. The improved TI method outperforms KB methods at identifying both beneficial and deleterious mutations. We observed that KB methods have higher accuracies in predicting deleterious mutations than beneficial mutations. A pipeline using KB methods to efficiently exclude deleterious mutations and TI to accurately identify beneficial mutations was developed for high-throughput mutation scanning. The pipeline was applied to optimize the binding affinity of a broadly sarbecovirus neutralizing antibody 10-40 against the circulating severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) omicron variant. Three identified beneficial mutations show strong synergy and improve both binding affinity and neutralization potency of antibody 10-40. Molecular dynamics simulation revealed that the three mutations improve the binding affinity of antibody 10-40 through the stabilization of an altered binding mode with increased polar and hydrophobic interactions. Above all, this study presents an accurate and efficient TI-based approach for optimizing antibodies and other biomolecules.

A computational quest for identifying potential vaccine candidates against Moraxella lacunata: a multi-pronged approach

Moraxella lacunata is an emerging gram-negative bacterium that is responsible for multiple nosocomial infections. The bacterium is evolving resistance to several antibiotics, and currently, no effective licensed vaccines are available, which warrants the search for new therapeutics. A multi-epitope-based vaccine has been designed for M. lacunata. The complete proteome of M. lacunata contains 10,110 core proteins. Subcellular localization analysis revealed the presence of five proteins in the extracellular matrix, while 19 proteins were predicted to be located in the outer membrane, and 21 proteins were predicted to be located in the periplasmic region. Only two proteins, the type VI secretion system tube protein (Hcp) and the transporter substrate-binding domain-containing protein, were selected for epitope prediction as they fulfilled all the criteria for being potential vaccine candidates. Shortlisted epitopes from the selected proteins were fused together using “GPGPG” linkers to overcome the limitations of single-epitope vaccines. Next, the cholera toxin-B adjuvant was attached to the peptide epitope using an EAAAK linker. Docking analysis was performed to examine the interaction between the vaccine and immune cell receptors, revealing robust intermolecular interactions and a stable binding conformation. Molecular dynamics simulation findings revealed no drastic changes in the binding conformation of complexes during the simulation period. The net binding free energy of vaccine-receptor complexes was estimated using the molecular mechanics energies combined with the Poisson–Boltzmann and surface area continuum solvation (MM-PBSA) method. The reported values were −586.38 kcal/mol, −283.74 kcal/mol, and −296.88 kcal/mol for the TLR-4-vaccine complex, MHC-I-vaccine complex, and MHC-II-vaccine complex, respectively. Furthermore, the molecular mechanics energies combined with the generalized Born and surface area continuum solvation (MM-GBSA) analysis predicted binding free energies of −596.69 kcal/mol, −287.39 kcal/mol, and −298.28 kcal/mol for the TLR-4-vaccine complex, MHC-I-vaccine complex, and MHC-II-vaccine complex, respectively. The theoretical vaccine design proposed in the study could potentially serve as a powerful therapeutic against targeted pathogens, subject to validation through experimental studies. Communicated by Ramaswamy H. Sarma

Predicting Protein-Ligand Binding and Unbinding Kinetics with Biased MD Simulations and Coarse-Graining of Dynamics: Current State and Challenges.

The prediction of drug-target binding and unbinding kinetics that occur on time scales between milliseconds and several hours is a prime challenge for biased molecular dynamics simulation approaches. This Perspective gives a concise summary of the theory and the current state-of-the-art of such predictions via biased simulations, of insights into the molecular mechanisms defining binding and unbinding kinetics as well as of the extraordinary challenges predictions of ligand kinetics pose in comparison to binding free energy predictions.

Combining QM/MM Calculations with Classical Mining Minima to Predict Protein-Ligand Binding Free Energy.

We developed an effective binding free energy prediction protocol which incorporates quantum mechanical/molecular mechanical (QM/MM) calculations to substitute the specified atomic charges of force fields with quantum-mechanically recalculated ones at a proposed pose using a mining minima approach with the VeraChem mining minima engine. We tested this protocol using seven well-known targets with 147 different ligands and compared it with classical mining minima and the most popular binding free energy (BFE) methods using different metrics. Our new protocol, dubbed Qcharge-VM2, yielded an overall Pearson correlation of 0.86, which was better than all the methods examined. Qcharge-VM2 performed significantly better than implicit solvent-based methods, such as MM-GBSA and MM-PBSA, but not as good as explicit water-based free energy perturbation methods, such as FEP+, in terms of root-mean-square error, RMSE (1.75 kcal/mol) and mean unsigned error, MUE (1.39 kcal/mol) on a limited set of targets. However, our protocol is substantially less computationally demanding compared with FEP+. The combined accuracy and efficiency of our method can be valuable in drug discovery campaigns.