Protein-ligand Binding Free Energy Research Articles

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.

Calculation of protein-ligand binding entropies using a rule-based molecular fingerprint

The use of fast in silico prediction methods for protein-ligand binding free energies holds significant promise for the initial phases of drug development. Numerous traditional physics-based models (e.g., implicit solvent models), however, tend to either neglect or heavily approximate entropic contributions to binding due to their computational complexity. Consequently, such methods often yield imprecise assessments of binding strength. Machine learning models provide accurate predictions and can often outperform physics-based models. They, however, are often prone to overfitting, and the interpretation of their results can be difficult. Physics-guided machine learning models combine the consistency of physics-based models with the accuracy of modern data-driven algorithms. This work integrates physics-based model conformational entropies into a graph convolutional network. We introduce a new neural network architecture (a rule-based graph convolutional network) that generates molecular fingerprints according to predefined rules specifically optimized for binding free energy calculations. Our results on 100 small host-guest systems demonstrate significant improvements in convergence and preventing overfitting. We additionally demonstrate the transferability of our proposed hybrid model by training it on the aforementioned host-guest systems and then testing it on six unrelated protein-ligand systems. Our new model shows little difference in training set accuracy compared to a previous model but an order-of-magnitude improvement in test set accuracy. Finally, we show how the results of our hybrid model can be interpreted in a straightforward fashion.

Efficiency of membrane fusion inhibitors on different hemagglutinin subtypes: insight from a molecular dynamics simulation perspective

The challenge in vaccine development, along with drug resistance issues, has encouraged the search for new anti-influenza drugs targeting different viral proteins. Hemagglutinin (HA) glycoprotein, crucial in the viral replication cycle, has emerged as a promising therapeutic target. CBS1117 and JNJ4796 were reported to exhibit similar potencies against infectious group 1 influenza, which included H1 and H5 HAs; however, their potencies were significantly reduced against group 2 HA. This study aims to explore the molecular binding mechanisms and group specificity of these fusion inhibitors against both group 1 (H5) and group 2 (H3) HA influenza viruses using molecular dynamics simulations. CBS1117 and JNJ4796 exhibit stronger interactions with key residues within the H5 HA binding pocket compared to H3-ligand complexes. Hydrogen bonding and hydrophobic interactions involving residues, such as H381, Q401, T3251 (H5-CBS1117), T3181 (H5-JNJ4796), W212, I452, V482, and V522 predominantly contribute to stabilizing H5-ligand systems. In contrast, these interactions are notably weakened in H3-inhibitor complexes. Predicted protein-ligand binding free energies align with experimental data, indicating CBS1117 and JNJ4796's preference for heterosubtypic group 1 HA binding. Understanding the detailed atomistic mechanisms behind the varying potencies of these inhibitors against the two HA groups can significantly contribute to the development and optimization of effective HA fusion inhibitors. To accomplish this, the knowledge of the transition of HA from its pre- to post-fusion states, the molecular size of ligands, and their potential binding regions, could be carefully considered. Communicated by Ramaswamy H. Sarma

Development and test of highly accurate endpoint free energy methods. 3: partition coefficient prediction using a Poisson-Boltzmann method combined with a solvent accessible surface area model for SAMPL challenges.

Accurately predicting solvation free energy is the key to predict protein-ligand binding free energy. In addition, the partition coefficient (log P), which is an important physicochemical property that determines the distribution of a drug in vivo, can be derived directly from transfer free energies, i.e., the difference between solvation free energies (SFEs) in different solvents. Within the Statistical Assessment of the Modeling of Proteins and Ligands (SAMPL) 9 challenge, we applied the Poisson-Boltzmann (PB) surface area (SA) approach to predict the toluene/water transfer free energy and partition coefficient (log Ptoluene/water) from SFEs. For each solute, only a single conformation automatically generated by the free software Open Babel was used. The PB calculation directly adopts our previously optimized boundary definition - a set of general AMBER force field 2 (GAFF2) atom-type based sphere radii for solute atoms. For the non-polar SA model, we newly developed the solvent-related molecular surface tension parameters γ and offset b for toluene and cyclohexane targeting experimental SFEs. This approach yielded the highest predictive accuracy in terms of root mean square error (RMSE) of 1.52 kcal mol-1 in transfer free energy for 16 small drug molecules among all 18 submissions in the SAMPL9 blind prediction challenge. The re-evaluation of the challenge set using multi-conformation strategies based on molecular dynamics (MD) simulations further reduces the prediction RMSE to 1.33 kcal mol-1. At the same time, an additional evaluation of our PBSA method on the SAMPL5 cyclohexane/water distribution coefficient (log Dcyclohexane/water) prediction revealed that our model outperformed COSMO-RS, the best submission model with RMSEPBSA = 1.88 versus RMSECOSMO-RS = 2.11 log units. Two external log Ptoluene/water and log Pcyclohexane/water datasets that contain 110 and 87 data points, respectively, are collected for extra validation and provide an in-depth insight into the error source of the PBSA method.

The maximal and current accuracy of rigorous protein-ligand binding free energy calculations

Computational techniques can speed up the identification of hits and accelerate the development of candidate molecules for drug discovery. Among techniques for predicting relative binding affinities, the most consistently accurate is free energy perturbation (FEP), a class of rigorous physics-based methods. However, uncertainty remains about how accurate FEP is and can ever be. Here, we present what we believe to be the largest publicly available dataset of proteins and congeneric series of small molecules, and assess the accuracy of the leading FEP workflow. To ascertain the limit of achievable accuracy, we also survey the reproducibility of experimental relative affinity measurements. We find a wide variability in experimental accuracy and a correspondence between binding and functional assays. When careful preparation of protein and ligand structures is undertaken, FEP can achieve accuracy comparable to experimental reproducibility. Throughout, we highlight reliable protocols that can help maximize the accuracy of FEP in prospective studies.

Exploration of human pancreatic alpha-amylase inhibitors from Physalis peruviana for the treatment of type 2 diabetes

Type 2 Diabetes (T2D), a chronic metabolic disorder characterized by persistent hyperglycemia, accounts for ∼90% of all types of diabetes. Pancreatic α-amylase is a potential drug target for preventing postprandial hyperglycemia and inhibiting T2D in humans. Although many synthetic drugs have been identified against pancreatic α-amylase, however, reported several side effects, and plant-derived natural products are less explored against T2D. This study tested 34 flavonoids derived from the plant Physalis peruviana against the human pancreatic α-amylase (HPA) using in silico computational approaches such as molecular docking and molecular dynamics simulation approaches. Schrödinger, a drug discovery package with modules applicable for molecular docking, protein-ligand interaction analysis, molecular dynamics, post-dynamics simulation, and binding free energy calculation, was employed for all computational studies. Four flavonoids, namely, Chlorogenic acid, Withaperuvin F, Withaperuvin H, and Rutin, were picked based on their docking score ranging between −7.03 kcal/mol and −11.35 kcal/mol compared to the docking score −7.3 kcal/mol of reference ligand, i.e. Myricetin. The molecular dynamics analysis suggested that all flavonoids showed considerable stability within the protein’s catalytic pocket, except chlorogenic acid, which showed high deviation during the last 15 ns. However, the interactions observed in initial docking and extracted from the simulation trajectory involved > 90% identical residues, indicating the affinity and stability of the docked flavonoids with the protein. Therefore, all four compounds identified in this study are proposed as promising antidiabetic candidates and should be further considered for their in vitro and in vivo validation. Communicated by Ramaswamy H. Sarma

Improving Speed and Affordability without Compromising Accuracy: Standard Binding Free-Energy Calculations Using an Enhanced Sampling Algorithm, Multiple-Time Stepping, and Hydrogen Mass Repartitioning.

Accurate evaluation of protein-ligand binding free energies in silico is of paramount importance for understanding the mechanisms of biological regulation and providing a theoretical basis for drug design and discovery. Based on a series of atomistic molecular dynamics simulations in an explicit solvent, using well-tempered metadynamics extended adaptive biasing force (WTM-eABF) as an enhanced sampling algorithm, the so-called "geometrical route" offers a rigorous theoretical framework for binding affinity calculations that match experimental values. However, although robust, this strategy remains expensive, requiring substantial computational time to achieve convergence of the simulations. Improving the efficiency of the geometrical route, while preserving its reliability through improved ergodic sampling, is, therefore, highly desirable. In this contribution, having identified the computational bottleneck of the geometrical route, to accelerate the calculations we combine (i) a longer time step for the integration of the equations of motion with hydrogen-mass repartitioning (HMR), and (ii) multiple time-stepping (MTS) for collective-variable and biasing-force evaluation. Altogether, we performed 50 independent WTM-eABF simulations in triplicate for the "physical" separation of the Abl kinase-SH3 domain:p41 complex, following different HMR and MTS schemes, while tuning, in distinct protocols, the parameters of the enhanced-sampling algorithm. To demonstrate the consistency and reliability of the results obtained with the best-performing setups, we carried out quintuple simulations. Furthermore, we demonstrated the transferability of our method to other complexes by triplicating a 200 ns separation simulation of nine chosen protocols for the MDM2-p53:NVP-CGM097 complex. [Holzer et al. J. Med. Chem. 2015, 58, 6348-6358.] Our results, based on an aggregate simulation time of 14.4 μs, allowed an optimal set of parameters to be identified, able to accelerate convergence by a factor of three without any noticeable loss of accuracy.

Development and Benchmarking of Open Force Field 2.0.0: The Sage Small Molecule Force Field.

We introduce the Open Force Field (OpenFF) 2.0.0 small molecule force field for drug-like molecules, code-named Sage, which builds upon our previous iteration, Parsley. OpenFF force fields are based on direct chemical perception, which generalizes easily to highly diverse sets of chemistries based on substructure queries. Like the previous OpenFF iterations, the Sage generation of OpenFF force fields was validated in protein-ligand simulations to be compatible with AMBER biopolymer force fields. In this work, we detail the methodology used to develop this force field, as well as the innovations and improvements introduced since the release of Parsley 1.0.0. One particularly significant feature of Sage is a set of improved Lennard-Jones (LJ) parameters retrained against condensed phase mixture data, the first refit of LJ parameters in the OpenFF small molecule force field line. Sage also includes valence parameters refit to a larger database of quantum chemical calculations than previous versions, as well as improvements in how this fitting is performed. Force field benchmarks show improvements in general metrics of performance against quantum chemistry reference data such as root-mean-square deviations (RMSD) of optimized conformer geometries, torsion fingerprint deviations (TFD), and improved relative conformer energetics (ΔΔE). We present a variety of benchmarks for these metrics against our previous force fields as well as in some cases other small molecule force fields. Sage also demonstrates improved performance in estimating physical properties, including comparison against experimental data from various thermodynamic databases for small molecule properties such as ΔHmix, ρ(x), ΔGsolv, and ΔGtrans. Additionally, we benchmarked against protein-ligand binding free energies (ΔGbind), where Sage yields results statistically similar to previous force fields. All the data is made publicly available along with complete details on how to reproduce the training results at https://github.com/openforcefield/openff-sage.

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.

Development and test of highly accurate endpoint free energy methods. 1: Evaluation of ABCG2 charge model on solvation free energy prediction and optimization of atom radii suitable for more accurate solvationfreeenergy prediction by the PBSA method.

Accurate estimation of solvation free energy (SFE) lays the foundation for accurate prediction of binding free energy. The Poisson-Boltzmann (PB) or generalized Born (GB) combined with surface area (SA) continuum solvation method (PBSA and GBSA) have been widely used in SFE calculations because they can achieve good balance between accuracy and efficiency. However, the accuracy of these methods can be affected by several factors such as the charge models, polar and nonpolar SFE calculation methods and the atom radii used in the calculation. In this work, the performance of the ABCG2 (AM1-BCC-GAFF2) charge model as well as other two charge models, that is, RESP (Restrained Electrostatic Potential) and AM1-BCC (Austin Model 1-bond charge corrections), on the SFE prediction of 544 small molecules in water by PBSA/GBSA was evaluated. In order to improve the performance of the PBSA prediction based on the ABCG2 charge, we further explored the influence of atom radii on the prediction accuracy and yielded a set of atom radius parameters for more accurate SFE prediction using PBSA based on the ABCG2/GAFF2 by reproducing the thermodynamic integration (TI) calculation results. The PB radius parameters of carbon, oxygen, sulfur, phosphorus, chloride, bromide and iodine, were adjusted. New atom types, on, oi, hn1, hn2, hn3, were introduced to further improve the fitting performance. Then, we tuned the parameters in the nonpolar SFE model using the experimental SFE data and the PB calculation results. By adopting the new radius parameters and new nonpolar SFE model, the root mean square error (RMSE) of the SFE calculation for the 544 molecules decreased from 2.38 to 1.05 kcal/mol. Finally, the new radius parameters were applied in the prediction of protein-ligand binding free energies using the MM-PBSA method. For the eight systems tested, we could observe higher correlation between the experiment data and calculation results and smaller prediction errors for the absolute binding free energies, demonstrating that our new radius parameters can improve the free energy calculation using the MM-PBSA method.

Selective probes and antagonists for G protein-coupled receptor LPAR1.

The G protein-coupled receptors (GPCRs) constitute the largest class of signaling molecules in the human genome with diverse disease pathogenesis, yet many of receptors with unknown antagonists continue to remain. The complex biology and potential for drug discovery within this class provides strong incentives for chemical biology approaches that seek to develop small molecule probes. In addition to this, it will also help to facilitate the elucidation of mechanistic pathways and enable the specific manipulation of the individual receptors’ activity. We have initiated a small molecule antagonist that actively targets lysophosphatidic acid receptor (LPAR1), a clinically important target that is linked to several metabolic and inflammatory diseases including cancer, fibrosis, and rheumatoid arthritis. While numerous selective LPAR1 antagonists have been developed their clinical efficacy has only been evaluated in idiopathic pulmonary fibrosis. As a useful aid in the various efforts of identifying novel effective LPAR1 antagonists, diverse computational methods can serve as an important and valuable tool. In particular, the sequent in-silico approaches such as virtual screening, molecular docking, molecular dynamics simulation, and protein-ligand binding free energy are able to refine the accuracy of in-silico approaches of GPCR-antagonist complexes. In our study, we focused on trying to identify novel LPAR1 antagonists using the in-silico virtual screening of commercial databases on the human LPAR1 model. Various molecular modeling methodologies such as molecular docking and dynamics simulation, along with different drug likeness filtering criteria, were applied to select anti-LPAR1 antagonists as promising candidate molecules based on different known lead compounds. In-vitro binding assays of the selected molecules are expected to demonstrate activity within the nanomolar range of Ki or IC50. The current integrated computational and experimental methods used in this work can provide new insights for systematic hit identification for novel anti-LPAR1 agents.

Development and benchmarking of an open, self-consistent force field for proteins and small molecules from the open force field initiative.

Contemporary protein force fields model canonical proteins well, but parameters for covalently modified residues are challenging to develop. Additionally, protein-specific parameters are derived separately from those for small molecules, limiting the transferability of small molecule parameters to protein contexts. A flexible force field that provides self-consistent parameters for proteins and small molecules would advance research in drug design and molecular mechanisms of protein structure and function. The Open Force Field (OpenFF) Initiative develops open, reproducible force fields for atomistic simulations, delivering automated infrastructure and systematic methodology for force field fitting and validation as well as version-controlled force field releases. OpenFF force fields assign parameters using direct chemical perception via the SMIRKS cheminformatics language, providing coverage of a large chemical space with fewer parameters than conventional atom-typed force fields. The most recent force field release, Sage (OpenFF 2.0.0), provides valence parameters trained against quantum chemical (QC) data and Lennard-Jones parameters trained against condensed phase properties of pure liquids and binary mixtures. Sage achieves competitive accuracy on benchmarks of the energies and geometries of QC minima, solvation free energies, and protein-ligand binding free energies. Here we describe the extension of Sage to proteins, resulting in a self-consistent force field that can simulate canonical and covalently modified proteins and small molecules. We used protein-specific SMIRKS to train proper torsions to two-dimensional QC scans of the backbone and sidechain dihedrals of capped peptides. The parameters were validated using benchmarks of published NMR observables for small peptides, folded proteins, and disordered proteins. Beyond force field parameters, the OpenFF software infrastructure now supports loading proteins from PDB files, iterating over hierarchies such as residues, and exporting parametrized systems to common molecular dynamics formats.

Advancing drug discovery with accurate and reliable protein-ligand binding free energy calculations.

Drug discovery is a very expensive and time-consuming process, with the average cost to bring a drug molecule into market reaching $2.5B in 2016. Computational modeling can significantly accelerate the preclinical discovery process and reduce the cost. In this presentation, I am going to discuss how computational modeling, particularly the accurate and reliable protein-ligand binding free energy calculations through FEP+, can help to address some of the challenges in preclinical drug discovery, and present a few real-life drug discovery projects demonstrating the various ways these computational models were able to impact the discovery process.

Multi-target molecular dynamic simulations reveal glutathione-S-transferase as the most favorable drug target of knipholone in Plasmodium falciparum

Knipholone is an antiplasmodial phytocompound obtained from the roots of Kniphofia foliosa. Despite several available studies, the molecular drug targets of knipholone in P. falciparum remained unknown. Nowadays, in silico techniques are widely used to study the molecular interactions between compounds and proteins as they provide results quickly with high precision and accuracy. In this study, we aim to identify the potential molecular drug targets of knipholone in P. falciparum. We selected 10 proteins of P. falciparum with unique metabolic functions and we found that knipholone showed better binding affinity than the native ligands of 6 proteins. Out of the 6 proteins, knipholone showed better enzyme inhibitory potential than the native ligands of 4 proteins. We carried out a 100 ns MD simulations for knipholone and the native ligands of four proteins and this was followed by binding free energy calculations. In each step, the performance of knipholone was compared to the native ligands of the proteins. Knipholone outperformed the native ligand of Glutathione-S-Transferase (1OKT) at crucial computational studies as evidence from the lower protein–ligand root mean square deviation value, protein root mean square fluctuation value, and protein–ligand binding free energies. The ligand properties of knipholone provide additional evidence for its stability and it maintains adequate protein–ligand contacts during the entire simulation. The density functional theory study also supported the stability of knipholone at the active binding site of 1OKT. From the studied proteins, we conclude that Glutathione-S-Transferase is the most favorable drug target for knipholone in P. falciparum. Communicated by Ramaswamy H. Sarma

ChemFlow─From 2D Chemical Libraries to Protein-Ligand Binding Free Energies.

The accurate prediction of protein-ligand binding affinities is a fundamental problem for the rational design of new drug entities. Current computational approaches are either too expensive or inaccurate to be effectively used in virtual high-throughput screening campaigns. In addition, the most sophisticated methods, e.g., those based on configurational sampling by molecular dynamics, require significant pre- and postprocessing to provide a final ranking, which hinders straightforward applications by nonexpert users. We present a novel computational platform named ChemFlow to bridge the gap between 2D chemical libraries and estimated protein-ligand binding affinities. The software is designed to prepare a library of compounds provided in SMILES or SDF format, dock them into the protein binding site, and rescore the poses by simplified free energy calculations. Using a data set of 626 protein-ligand complexes and GPU computing, we demonstrate that ChemFlow provides relative binding free energies with an RMSE < 2 kcal/mol at a rate of 1000 ligands per day on a midsize computer cluster. The software is publicly available at https://github.com/IFMlab/ChemFlow.

MM/PB(GB)SA benchmarks on soluble proteins and membrane proteins.

Predicting protein-ligand binding free energy rapidly and accurately remains a challenging question in modern drug discovery. Molecular mechanics/Poisson-Boltzmann (Generalized Born) surface area (MM/PB(GB)SA) has emerged as an essential tool for accelerating cost-efficient binding free energy calculation. This study presents benchmarks with three membrane-bound protein systems and six soluble protein systems. Different parameters were sampled for different benchmarks to explore the highest accuracy. These include ligand charges, protein force fields, extra points, GB models, nonpolar optimization methods, internal dielectric constants and membrane dielectric constants. Comparisons of accuracy were made between MM/PB(GB)SA, docking and free energy perturbation (FEP). The results reveal a competitive performance between MM/PB(GB)SA and FEP. In summary, MM/PB(GB)SA is a powerful approach to predict ligand binding free energy rapidly and accurately. Parameters of MM/PB(GB)SA calculations, such as the GB models and membrane dielectric constants, need to be optimized for different systems. This method can be served as a powerful tool for drug design.

Practical Guidance for Consensus Scoring and Force Field Selection in Protein-Ligand Binding Free Energy Simulations.

The advances in ligand binding affinity prediction have been fostered by system generation tools and improved force fields (FFs). CHARMM-GUI Free Energy Calculator provides input and postprocessing scripts for AMBER-TI free energy calculations with various FFs. In this study, we used 12 different FF combinations (ff14SB and ff19SB for protein, GAFF2.2 and OpenFF for ligand, and TIP3P, TIP4PEW, and OPC for water) to calculate relative binding free energies (ΔΔGbind) for 80 alchemical transformations (among the JACS benchmark set) with different numbers of λ windows (12 or 21) and simulation times (1, 5, or 10 ns). Our results show that 12 λ windows and 5 ns simulation time for each window are sufficient to obtain reliable ΔΔGbind with 4 independent runs for the current benchmark set. While there is no statistically noticeable performance difference among 12 different FF combinations compared to the experimental values, a combination of ff14SB + GAFF2.2 + TIP3P FFs appears to be best with a mean unsigned error of 0.87 [0.69, 1.07] kcal/mol, a root-mean-square error of 1.22 [0.94, 1.50] kcal/mol, a Pearson's correlation of 0.64 [0.52, 0.76], a Spearman's correlation of 0.73 [0.58, 0.83], and a Kendell's correlation of 0.54 [0.42, 0.64]. This large-scale ΔΔGbind calculation study provides useful information about ΔΔGbind prediction with different AMBER FF combinations and presents valuable suggestions for FF selection in AMBER-TI ΔΔGbind calculations.

Meta-Analysis Reveals That Absolute Binding Free-Energy Calculations Approach Chemical Accuracy.

Systematic and quantitative analysis of the reliability of formally exact methods that in silico calculate absolute protein-ligand binding free energies remains lacking. Here, we provide, for the first time, evidence-based information on the reliability of these methods by statistically studying 853 cases from 34 different research groups through meta-analysis. The results show that formally exact methods approach chemical accuracy (error = 1.58 kcal/mol), even if people are challenging difficult tasks such as blind drug screening in recent years. The geometrical-pathway-based methods prove to possess a better convergence ability than the alchemical ones, while the latter have a larger application range. We also reveal the importance of always using the latest force fields to guarantee reliability and discuss the pros and cons of turning to an implicit solvent model in absolute binding free-energy calculations. Moreover, based on the meta-analysis, an evidence-based guideline for in silico binding free-energy calculations is provided.

Insights into binding molecular mechanism of hemagglutinin H3N2 of influenza virus complexed with arbidol and its derivative: A molecular dynamics simulation perspective

Recently, the H3N2 influenza outbreak has caused serious global public health concern for future control of the next influenza pandemic. Since using current anti-influenza drugs targeting neuraminidase (oseltamivir and zanamivir) and the proton M2 channel (amantadine and rimantadine) leads to drug resistance, it is essential to seek new anti-viral agents that act on additional viral targets. Hemagglutinin (HA), a glycoprotein embedded in the viral surface and playing a critical role in influenza the viral replication cycle has become an attractive target. This work investigates the molecular binding mechanism of HA H3N2 of influenza virus complexed with the fusion inhibitor, arbidol and its derivative (der-arbidol), by means of molecular dynamics simulation. The result showed that the arbidol derivative could form many and strong hydrogen bonds with the HA surrounding amino acids comprising GLU1032(1), LYS3071(1) and LYS3102(1) while arbidol makes this type of interaction with only LYS582(1). The introduction of hydroxyl group at the meta-position of the thiophenol ring was detected to replace the nearby water molecule, thus allowing the direct hydrogen bond formation between der-arbidol and GLU1032(1) of HA residue. Furthermore, the salt bridge networks established among residues GLU572(1)···ARG542(1)···GLU972(2) were considerably more stable in HA-Der-arbidol than that found in HA-Arbidol. The predicted protein-ligand binding free energies were in agreement with experimental data indicating that der-arbidol exhibits higher inhibitory potency against HA H3N2 of influenza virus. Detailed information could be useful for further designing and optimizing HA fusion inhibitors with improved efficiency.

Identification of Novel AXL Kinase Inhibitors Using Ligand-Based Pharmacophore Screening and Molecular Dynamics Simulations

AXL kinase is a promising target in novel drug discovery for cancer. A ligand-based pharmacophore model was generated with the Pharmit web server. Its inbuilt PubChem molecule database was screened and led to 408 candidate molecules. Docking of the AXL kinase active sites with the identified list of candidate molecules was carried out with Autodock Vina docking software. This resulted in four compounds selected for further investigation. Molecular dynamics simulation of two ligands (PubChem-122421875 and PubChem-78160848) showed considerable binding with AXL kinase. From the MM-PBSA binding free energies investigation, the PubChem-122421875 (G = −179.3 kJ/mol) and PubChem-78160848 (G = −208.3 kJ/mol) ligands had favorable protein-ligand complex stability and binding free energy. Hence, PubChem-122421875 and PubChem-78160848 molecules identified in this work could be a potent starting point for developing novel AXL kinase inhibitor molecules.