Binding Free Energy Calculations for Lead Optimization: Assessment of Their Accuracy in an Industrial Drug Design Context

Computational discovery of tripeptide inhibitors targeting monkeypox virus A42R profilin-like protein

Monkeypox is an infectious disease caused by the monkeypox virus (MPXV), a member of the Orthopoxvirus genus closely related to smallpox. The structure of the A42R profilin-like protein is the first and only available structure among MPXV proteins. Biochemical studies of A42R were conducted in the 1990s and later work also analyzed the protein's function in viral replication in cells. This study aims to screen tripeptides for their potential inhibition of the A42R profilin-like protein using computational methods, with implications for MPXV therapy. A total of 8000 tripeptides underwent molecular docking simulations, resulting in the identification of 20 compounds exhibiting strong binding affinity to A42R. To validate the docking results, molecular dynamics simulations and free energy perturbation calculations were performed. These analyses revealed two tripeptides with sequences TRP-THR-TRP and TRP-TRP-TRP, which displayed robust binding affinity to A42R. Markedly, electrostatic interactions predominated over van der Waals interactions in the binding process between tripeptides and A42R. Three A42R residues, namely Glu9, Ser12, and Arg38, appear to be pivotal in mediating the interaction between A42R and the tripeptide ligands. Notably, tripeptides containing two or three tryptophan residues demonstrate a pronounced binding affinity, with the tripeptide comprising three tryptophan amino acids showing the highest level of affinity. These findings offer valuable insights for the selection of compounds sharing a similar structure and possessing a high affinity for A42R, potentially capable of inhibiting its enzyme activity. The study highlights a structural advantage and paves the way for the development of targeted therapies against MPXV infections.

A Study on the Binding Mechanism and the Impact of Key Residue Mutations between SND1 and MTDH Peptide through Molecular Dynamics Simulations

Metastasis of breast cancer is the main cause of death for patients with breast cancer. The interaction between metadherin (MTDH) and staphylococcal nuclease domain 1 (SND1) plays a pivotal role in promoting breast cancer development. However, the binding details between MTDH and SND1 remain unclear. In this study, we employed all-atom molecular dynamics simulations (MDs) and conducted binding energy calculations to investigate the binding details and the impact of key residue mutations on binding. The mutations in key residues have not significantly affected the overall stability of the structure and the fluctuation of residues near the binding site; they have exerted a substantial impact on the binding of SND1 and MTDH peptide. The electrostatic interactions and van der Waals interactions play an important role in the binding of SND1 and the MTDH peptide. The mutations in the key residues have a significant impact on electrostatic and van der Waals interactions, resulting in weakened binding. The energy contributions of key residues mainly come from the electrostatic energy and van der Waals interactions of the side chain. In addition, the key residues form an intricate and stable network of hydrogen bonds and salt-bridge interactions with the MTDH peptide. The mutations in key residues have directly disrupt the interactions formed between SND1 and MTDH peptide, consequently leading to changes in the binding mode of the MTDH peptide. These analyses unveil the detailed atomic-level interaction mechanism between SND1 and the MTDH peptide, providing a molecular foundation for the development of antibreast cancer drugs.

Virtual screening, molecular docking and dynamics simulation studies to identify potential agonists of orphan receptor GPR78 targeting CNS disorders

G protein-coupled receptors (GPCRs) are important targets in drug discovery because of their roles in physiological and pathological processes. Orphan GPCRs are GPCR proteins for which endogenous ligands have not yet been identified and they present interesting avenues for therapeutic intervention. This study focuses on GPR78, an orphan GPCR that is expressed in the central nervous system and linked to neurological disorders. GPR78 has no reported crystal structure and there is limited research. In this study, we have predicted the three dimensional model of GPR78 and its probable binding pocket. Structure-based virtual screening was carried out using the ChemDiv and Enamine REAL databases, followed by induced-fit docking studies to identify potential lead compounds with favorable interactions. These lead compounds were then embedded into a POPC lipid bilayer for a 200 ns molecular dynamics simulation. Free energy landscapes and MM-PBSA analyses were performed to assess the binding energies and conformational dynamics. The results highlight the dynamic nature of GPR78 in the presence of lead compounds and show favorable binding interactions. This study aims to predict a reliable three dimensional model of GPR78 and identify novel lead compounds through a comprehensive in silico approach. The identification of these potential GPR78 agonists represents a significant step in the development of new therapeutics for neurological disorders, highlighting the therapeutic potential of orphan GPR78 in CNS disorders.

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.

Automated Absolute Binding Free Energy Calculation Workflow for Drug Discovery

Absolute binding free energies play a crucial role in drug development, particularly as part of the lead discovery process. In recent work, we showed how in silico predictions directly could support drug development by ranking and recommending favorable ideas over unfavorable ones. Here, we demonstrate a Python workflow that enables the calculation of ABFEs with minimal manual input effort, such as the receptor PDB and ligand SDF files, and outputs a .tsv file containing the ranked ligands and their corresponding binding free energies. The implementation uses Snakemake to structure and control the execution of tasks, allowing for dynamic control of parameters and execution patterns. We provide an example of a benchmark system that demonstrates the effectiveness of the automated workflow.

Exploring binding modes of the selected inhibitors to SND1 by all-atom molecular dynamics simulations

Breast cancer is the leading cause of cancer-related deaths in women. Previous studies have indicated that disrupting the interaction between Metadherin (MTDH) and Staphylococcal nuclease domain containing 1 (SND1) can inhibit breast cancer development. Understanding the binding mode of small molecule inhibitors with SND1 is of great significance for designing drugs targeting the MTDH-SND1 complex. In this study, we conducted all-atom molecular dynamics (MD) simulations in solution and performed binding energy calculations to gain insights into the binding mechanism of small molecules to SND1. The binding site of SND1 for small molecules is relatively rigid, and the binding of the small molecule and the mutation of key residues have little effect on the conformation of the binding site. SND1 binds more tightly to C26-A6 than to C26-A2, as C26-A2 undergoes a 180° directional change during the simulation process. The key residue mutations have a direct effect on the position and orientation of small molecule in the binding site. The key residues make primary contributions to the binding energy through van der Waals interaction and nonpolar solvation energy, although the contribution from nonpolar solvation is relatively minor. The key residue mutations also affect the formation of hydrogen bonds and ultimately the stability of the small molecule-SND1 complex.Communicated by Ramaswamy H. Sarma.

Machine learning combines atomistic simulations to predict SARS-CoV-2 Mpro inhibitors from natural compounds

To date, the COVID-19 pandemic has still been infectious around the world, continuously causing social and economic damage on a global scale. One of the most important therapeutic targets for the treatment of COVID-19 is the main protease (Mpro) of SARS-CoV-2. In this study, we combined machine-learning (ML) model with atomistic simulations to computationally search for highly promising SARS-CoV-2 Mpro inhibitors from the representative natural compounds of the National Cancer Institute (NCI) Database. First, the trained ML model was used to scan the library quickly and reliably for possible Mpro inhibitors. The ML output was then confirmed using atomistic simulations integrating molecular docking and molecular dynamic simulations with the linear interaction energy scheme. The results turned out to show that there was evidently good agreement between ML and atomistic simulations. Ten substances were proposed to be able to inhibit SARS-CoV-2 Mpro. Seven of them have high-nanomolar affinity and are very potential inhibitors. The strategy has been proven to be reliable and appropriate for fast prediction of SARS-CoV-2 Mpro inhibitors, benefiting for new emerging SARS-CoV-2 variants in the future accordingly.

Molecular Mechanisms Underlying Medium-Chain Free Fatty Acid-Regulated Activity of the Phospholipase PlaF from Pseudomonas aeruginosa

PlaF is a membrane-bound phospholipase A1 from Pseudomonas aeruginosa that is involved in remodeling membrane glycerophospholipids (GPLs) and modulating virulence-associated signaling and metabolic pathways. Previously, we identified the role of medium-chain free fatty acids (FFAs) in inhibiting PlaF activity and promoting homodimerization, yet the underlying molecular mechanism remained elusive. Here, we used unbiased and biased molecular dynamics simulations and free energy computations to assess how PlaF interacts with FFAs localized in the water milieu surrounding the bilayer or within the bilayer and how these interactions regulate PlaF activity. Medium-chain FFAs localized in the upper bilayer leaflet can stabilize inactive dimeric PlaF, likely through interactions with charged surface residues, as has been experimentally validated. Potential of mean force (PMF) computations indicate that membrane-bound FFAs may facilitate the activation of monomeric PlaF by lowering the activation barrier for changing into a tilted, active configuration. We estimated that the coupled equilibria of PlaF monomerization-dimerization and tilting at the physiological concentration of PlaF lead to the majority of PlaF forming inactive dimers when in a cell membrane loaded with decanoic acid (C10). This is in agreement with a suggested in vivo product feedback loop and gas chromatography-mass spectrometry profiling results, indicating that PlaF catalyzes the release of C10 from P. aeruginosa membranes. Additionally, we found that C10 in the water milieu can access the catalytic site of active monomeric PlaF, contributing to the competitive component of C10-mediated PlaF inhibition. Our study provides mechanistic insights into how medium-chain FFAs may regulate the activity of PlaF, a potential bacterial drug target.

Identification of potent BRD4-BD1 inhibitors using classical and steered molecular dynamics based free energy analysis

In the present work a combination of traditional and steered molecular dynamics based techniques were employed to identify potential inhibitors against the human BRD4 protein (BRD4- BD1); an established drug target for multiple illnesses including various malignancies. Quinoline derivatives that were synthesized in-house were tested for their potential as new BRD4-BD1 inhibitors. Initially molecular docking experiments were performed to determine the binding poses of BRD4-BD1 inhibitors. To learn more about the thermodynamics of inhibitor binding to the BRD4-BD1 active site, the Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) free energy calculations were conducted afterwards. The findings of the MM-PBSA analysis were further reinforced by performing steered umbrella sampling simulations which revealed crucial details about the binding/unbinding process of the most potent quinoline derivatives at the BRD4-BD1 active site. We report a novel quinoline derivative which can be developed into a fully functional BRD4-BD1 inhibitor after experimental validation. The identified compound (4 g) shows better properties than the standard BRD4-BD1 inhibitors considered in the study. The study also highlights the crucial role of Gln78, Phe79, Trp81, Pro82, Phe83, Gln84, Gln85, Val87, Leu92, Leu94, Tyr97, Met105, Cys136, Asn140, Ile146 and Met149 in inhibitor binding. The study provides a possible lead candidate and key amino acids involved in inhibitor recognition and binding at the active site of BRD4-BD1 protein. The findings might be of significance to medicinal chemists involved in the development of potent BRD4-BD1 inhibitors.

Natural compounds inhibit Monkeypox virus methyltransferase VP39 in silico studies

VP39, an essential 2'-O-RNA methyltransferase enzyme discovered in Monkeypox virus (MPXV), plays a vital role in viral RNA replication and transcription. Inhibition of the enzyme may prevent viral replication. In this context, using a combination of molecular docking and molecular dynamics (MDs) simulations, the inhibitory ability of NCI Diversity Set VII natural compounds to VP39 protein was investigated. It should be noted that the computed binding free energy of ligand via molecular docking and linear interaction energy (LIE) approaches are in good agreement with the corresponding experiments with coefficients of R = 0.72 and 0.75, respectively. NSC 319990, NSC 196515 and NSC 376254 compounds were demonstrated that can inhibit MPVX methyltransferase VP39 protein with the similar affinity compared to available inhibitor sinefungin. Moreover, nine residues involving Gln39, Gly68, Gly72, Asp95, Arg97, Val116, Asp138, Arg140 and Asn156 may be argued that they play an important role in binding process of inhibitors to VP39.Communicated by Ramaswamy H. Sarma.

Accurate calculation of affinity changes to the close state of influenza A M2 transmembrane domain in response to subtle structural changes of adamantyl amines using free energy perturbation methods in different lipid bilayers

Experimental binding free energies of 27 adamantyl amines against the influenza M2(22-46) WT tetramer, in its closed form at pH 8, were measured by ITC in DPC micelles. The measured Kd's range is ~44 while the antiviral potencies (IC50) range is ~750 with a good correlation between binding free energies computed with Kd and IC50 values (r = 0.76). We explored with MD simulations (ff19sb, CHARMM36m) the binding profile of complexes with strong, moderate and weak binders embedded in DMPC, DPPC, POPC or a viral mimetic membrane and using different experimental starting structures of M2. To predict accurately differences in binding free energy in response to subtle changes in the structure of the ligands, we performed 18 alchemical perturbative single topology FEP/MD NPT simulations (OPLS2005) using the BAR estimator (Desmond software) and 20 dual topology calculations TI/MD NVT simulations (ff19sb) using the MBAR estimator (Amber software) for adamantyl amines in complex with M2(22-46) WT in DMPC, DPPC, POPC. We observed that both methods with all lipids show a very good correlation between the experimental and calculated relative binding free energies (r = 0.77-0.87, mue = 0.36-0.92 kcal mol-1) with the highest performance achieved with TI/MBAR and lowest performance with FEP/BAR in DMPC bilayers. When antiviral potencies are used instead of the Kd values for computing the experimental binding free energies we obtained also good performance with both FEP/BAR (r = 0.83, mue = 0.75 kcal mol-1) and TI/MBAR (r = 0.69, mue = 0.77 kcal mol-1).

Computational Workflow for Refining AlphaFold Models in Drug Design Using Kinetic and Thermodynamic Binding Calculations: A Case Study for the Unresolved Inactive Human Adenosine A3 Receptor

A structure-based drug design pipeline that considers both thermodynamic and kinetic binding data of ligands against a receptor will enable the computational design of improved drug molecules. For unresolved GPCR-ligand complexes, a workflow that can apply both thermodynamic and kinetic binding data in combination with alpha-fold (AF)-derived or other homology models and experimentally resolved binding modes of relevant ligands in GPCR-homologs needs to be tested. Here, as test case, we studied a congeneric set of ligands that bind to a structurally unresolved G protein-coupled receptor (GPCR), the inactive human adenosine A3 receptor (hA3R). We tested three available homology models from which two have been generated from experimental structures of hA1R or hA2AR and one model was a multistate alphafold 2 (AF2)-derived model. We applied alchemical calculations with thermodynamic integration coupled with molecular dynamics (TI/MD) simulations to calculate the experimental relative binding free energies and residence time (τ)-random accelerated MD (τ-RAMD) simulations to calculate the relative residence times (RTs) for antagonists. While the TI/MD calculations produced, for the three homology models, good Pearson correlation coefficients, correspondingly, r = 0.74, 0.62, and 0.67 and mean unsigned error (mue) values of 0.94, 1.31, and 0.81 kcal mol-1, the τ-RAMD method showed r = 0.92 and 0.52 for the first two models but failed to produce accurate results for the multistate AF2-derived model. With subsequent optimization of the AF2-derived model by reorientation of the side chain of R1735.34 located in the extracellular loop 2 (EL2) that blocked ligand's unbinding, the computational model showed r = 0.84 for kinetic data and improved performance for thermodynamic data (r = 0.81, mue = 0.56 kcal mol-1). Overall, after refining the multistate AF2 model with physics-based tools, we were able to show a strong correlation between predicted and experimental ligand relative residence times and affinities, achieving a level of accuracy comparable to an experimental structure. The computational workflow used can be applied to other receptors, helping to rank candidate drugs in a congeneric series and enabling the prioritization of leads with stronger binding affinities and longer residence times.

Crystal Packing Reveals a Potential Autoinhibited KRAS Dimer Interface and a Strategy for Small-Molecule Inhibition of RAS Signaling

KRAS GTPases harbor oncogenic mutations in more than 25% of human tumors. KRAS is considered to be largely undruggable due to the lack of a suitable small-molecule binding site. Here, we report a unique crystal structure of His-tagged KRASG12D that reveals a remarkable conformational change. The Switch I loop of one His-KRASG12D structure extends into the Switch I/II pocket of another His-KRASG12D in an adjacent unit cell to create an elaborate interface that is reminiscent of high-affinity protein-protein complexes. We explore the contributions of amino acids at this interface using alanine-scanning studies with alchemical free energy perturbation calculations based on explicit-solvent molecular dynamics simulations. Several interface amino acids were found to be hot spots as they contributed more than 1.5 kcal/mol to the protein-protein interaction. Computational analysis of the complex revealed the presence of two large binding pockets that possess physicochemical features typically found in pockets considered druggable. Small-molecule binding to these pockets may stabilize this autoinhibited structure of KRAS if it exists in cells to provide a new strategy to inhibit RAS signaling.

Discovery of Nonretinoid Inhibitors of CRBP1: Structural and Dynamic Insights for Ligand-Binding Mechanisms

The dysregulation of retinoid metabolism has been linked to prevalent ocular diseases including age-related macular degeneration and Stargardt disease. Modulating retinoid metabolism through pharmacological approaches holds promise for the treatment of these eye diseases. Cellular retinol-binding protein 1 (CRBP1) is the primary transporter of all-trans-retinol (atROL) in the eye, and its inhibition has recently been shown to protect mouse retinas from light-induced retinal damage. In this report, we employed high-throughput screening to identify new chemical scaffolds for competitive, nonretinoid inhibitors of CRBP1. To understand the mechanisms of interaction between CRBP1 and these inhibitors, we solved high-resolution X-ray crystal structures of the protein in complex with six selected compounds. By combining protein crystallography with hydrogen/deuterium exchange mass spectrometry, we quantified the conformational changes in CRBP1 caused by different inhibitors and correlated their magnitude with apparent binding affinities. Furthermore, using molecular dynamic simulations, we provided evidence for the functional significance of the "closed" conformation of CRBP1 in retaining ligands within the binding pocket. Collectively, our study outlines the molecular foundations for understanding the mechanism of high-affinity interactions between small molecules and CRBPs, offering a framework for the rational design of improved inhibitors for this class of lipid-binding proteins.

Structure-based virtual screening of natural compounds as inhibitors of HCV using molecular docking and molecular dynamics simulation studies

The hepatitis C virus (HCV), which causes hepatitis C, is a viral infection that damages the liver and causes inflammation in the liver. New potentially effective antiviral drugs are required for its treatment owing to various issues associated with the existing medications, including moderate to severe adverse effects, higher costs, and the emergence of drug-resistant strains. The objective of the current study was to utilize computational techniques to assess the anti-HCV efficacy of certain phytochemicals against tetraspanin (CD81) and claudin 1 (CLDN1) entry proteins. A 200-nanosecond molecular dynamics (MD) simulation was employed to examine the stability of the lead-protein complexes. Free binding energy and molecular docking calculations were conducted utilizing MM/GBSA method, and the selectivity of hit compounds for CD81 and CLDN1 was determined. Five significant CD81 and CLDN1 inhibitors were identified: Petasiphenone, Silibinin, Tanshinone IIA, Taxifolin, and Topaquinone. The MM/GBSA analysis of the compounds revealed high free binding energies. All the identified compounds were stable within the CD81 and CLDN1 binding pockets. This study indicated the promising inhibitory potential of the identified compounds against CD81 and CLDN1 receptors and might develop into potential viral entry inhibitors. However, to validate the chemotherapeutic capabilities of the discovered leads extensive preclinical research is required.Communicated by Ramaswamy H. Sarma.

Assessing the performance of docking, FEP, and MM/GBSA methods on a series of KLK6 inhibitors

Kallikrein 6 (KLK6) is an attractive drug target for the treatment of neurological diseases and for various cancers. Herein, we explore the accuracy and efficiency of different computational methods and protocols to predict the free energy of binding (ΔG_bind) for a series of 49 inhibitors of KLK6. We found that the performance of the methods varied strongly with the tested system. For only one of the three KLK6 datasets, the docking scores obtained with rDock were in good agreement (R² ≥ 0.5) with experimental values of ΔG_bind. A similar result was obtained with MM/GBSA (using the ff14SB force field) calculations based on single minimized structures. Improved binding affinity predictions were obtained with the free energy perturbation (FEP) method, with an overall MUE and RMSE of 0.53 and 0.68 kcal/mol, respectively. Furthermore, in a simulation of a real-world drug discovery project, FEP was able to rank the most potent compounds at the top of the list. These results indicate that FEP can be a promising tool for the structure-based optimization of KLK6 inhibitors.

Integrated molecular modeling and dynamics approaches revealed potential natural inhibitors of NF-κB transcription factor as breast cancer therapeutics

Breast cancer is a silent killer malady among women and a serious economic burden in health care management. A case of breast cancer is diagnosed among women every 19 s, and every 74 s, a woman dies of breast cancer somewhere in the world. Despite the pop-up of progressive research, advanced treatment approaches, and preventive measures, breast cancer remains amplifying ailment. The nuclear factor kappa B (NF-κB) is a key transcription factor that links inflammation with cancer and is demonstrated as being involved in the tumorigenesis of breast cancer. The NF-κB transcription factor family in mammals consists of five proteins; c-Rel, RelA(p65), RelB, NF-κB1(p50), and NF-κB2(p52). The antitumor effect of NF-κB has also been explored in breast cancer, however, the actual treatment for breast cancer is yet to be discovered. This study is attributed to the identification of novel drug targets against breast cancer by targeting c-Rel, RelA(p65), RelB, NF-κB1(p50), and NF-κB2(p52) proteins. To identify the putative active compounds, a structure-based 3D pharmacophore model to the protein active site cavity was generated followed by virtual screening, molecular docking, and molecular dynamics (MD) simulation. Initially, a library of 45000 compounds were docked against the target protein and five compounds namely Z56811101, Z653426226, Z1097341967, Z92743432, and Z464101066 were selected for further analysis. The relative binding affinity of Z56811101, Z653426226, Z1097341967, Z92743432, and Z464101066 with NF-κB1 (p50), NF-κB2 (p52), RelA (p65), RelB, and c-Rel proteins were -6.8, -8, -7.0, -6.9, and -7.2 kcal/mol, respectively which remained stable throughout the simulations of 200 ns. Furthermore, all of these compounds depict maximum drug-like properties. Therefore, the proposed compounds can be a potential candidate for patients with breast cancer, but, experimental validation is needed to ensure their safety.Communicated by Ramaswamy H. Sarma.

Revisiting MMPBSA by Adoption of MC-Based Surface Area/Volume, ANI-ML Potentials, and Two-Valued Interior Dielectric Constant

Here, we report the accuracy improvements of molecular mechanics Poisson-Boltzmann surface area (MMPBSA) calculations by adoption of ANI-ML potentials in replacement of MM terms, the use of solvent-accessible surface area (SASA) and volume (SAV) values from the Monte Carlo sampling of the probe, and introducing two different interior dielectric constants for electrostatic interactions of protein-ligand (P-L) and polar solvation term in the MMPBSA calculations. Our results show that the Pearson correlation coefficients of MMPBSA-calculated values with respect to experimental binding free energies can be drastically improved from 0.48 to 0.90 by adoption of ANI-ML potentials in replacement of MM energy terms in the equation, referred to as ANI-PBSA. Moreover, we show that the SASA/SAV-combined equation in the scaled particle theory (SPT) can be a better choice to model nonpolar solvation term, reaching nearly the same accuracy by ANI-PBSA calculations. Finally, we introduce two different values of interior dielectric constants, which could be an alternative strategy between the single and variable constant definitions.

Scaffold Hopping and Optimization of Small Molecule Soluble Adenyl Cyclase Inhibitors Led by Free Energy Perturbation

Free energy perturbation is a computational technique that can be used to predict how small changes to an inhibitor structure will affect the binding free energy to its target. In this paper, we describe the utility of free energy perturbation with FEP+ in the hit-to-lead stage of a drug discovery project targeting soluble adenyl cyclase. The project was structurally enabled by X-ray crystallography throughout. We employed free energy perturbation to first scaffold hop to a preferable chemotype and then optimize the binding affinity to sub-nanomolar levels while retaining druglike properties. The results illustrate that effective use of free energy perturbation can enable a drug discovery campaign to progress rapidly from hit to lead, facilitating proof-of-concept studies that enable target validation.

Synthesis, molecular docking, and binding Gibbs free energy calculation of β-nitrostyrene derivatives: Potential inhibitors of SARS-CoV-2 3CL protease

The outbreak of novel coronavirus disease 2019 (COVID-19), caused by the novel coronavirus (SARS-CoV-2), has had a significant impact on human health and the economic development. SARS-CoV-2 3CL protease (3CLpro) is highly conserved and plays a key role in mediating the transcription of virus replication. It is an ideal target for the design and screening of anti-coronavirus drugs. In this work, seven β-nitrostyrene derivatives were synthesized by Henry reaction and β-dehydration reaction, and their inhibitory effects on SARS-CoV-2 3CL protease were identified by enzyme activity inhibition assay in vitro. Among them, 4-nitro-β-nitrostyrene (compound a) showed the lowest IC₅₀ values of 0.7297 μM. To investigate the key groups that determine the activity of β-nitrostyrene derivatives and their interaction mode with the receptor, the molecular docking using the CDOCKER protocol in Discovery Studio 2016 was performed. The results showed that the hydrogen bonds between β-NO₂ and receptor GLY-143 and the π-π stacking between the aryl ring of the ligand and the imidazole ring of receptor HIS-41 significantly contributed to the ligand activity. Furthermore, the ligand-receptor absolute binding Gibbs free energies were calculated using the Binding Affinity Tool (BAT.py) to verify its correlation with the activity of β-nitrostyrene 3CLpro inhibitors as a scoring function. The higher correlation(r²=0.6) indicates that the absolute binding Gibbs free energy based on molecular dynamics can be used to predict the activity of new β-nitrostyrene 3CLpro inhibitors. These results provide valuable insights for the functional group-based design, structure optimization and the discovery of high accuracy activity prediction means of anti-COVID-19 lead compounds.

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 solvation free energy 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.

Characterizing the ligand-binding affinity toward SARS-CoV-2 Mpro via physics- and knowledge-based approaches

Computational approaches, including physics- and knowledge-based methods, have commonly been used to determine the ligand-binding affinity toward SARS-CoV-2 main protease (Mpro or 3CLpro). Strong binding ligands can thus be suggested as potential inhibitors for blocking the biological activity of the protease. In this context, this paper aims to provide a short review of computational approaches that have recently been applied in the search for inhibitor candidates of Mpro. In particular, molecular docking and molecular dynamics (MD) simulations are usually combined to predict the binding affinity of thousands of compounds. Quantitative structure-activity relationship (QSAR) is the least computationally demanding and therefore can be used for large chemical collections of ligands. However, its accuracy may not be high. Moreover, the quantum mechanics/molecular mechanics (QM/MM) method is most commonly used for covalently binding inhibitors, which also play an important role in inhibiting the activity of SARS-CoV-2. Furthermore, machine learning (ML) models can significantly increase the searching space of ligands with high accuracy for binding affinity prediction. Physical insights into the binding process can then be confirmed via physics-based calculations. Integration of ML models into computational chemistry provides many more benefits and can lead to new therapies sooner.

Assessing the effect of forcefield parameter sets on the accuracy of relative binding free energy calculations

Software for accurate prediction of protein-ligand binding affinity can be a key enabling tool for small molecule drug discovery. Free energy perturbation (FEP) is a computational technique that can be used to compute binding affinity differences between molecules in a congeneric series. It has shown promise in reliably generating accurate predictions and is now widely used in the pharmaceutical industry. However, the high computational cost and use of commercial software, together with the technical challenges to setup, run, and analyze the simulations, limits the usage of FEP. Here, we use an automated FEP workflow which uses the open-source OpenMM package. To enable effective application of FEP, we compared the performance of different water models, partial charge assignments, and AMBER protein forcefields in eight benchmark test cases previously assembled for FEP validation studies.

Total Synthesis of the Antimycobacterial Natural Product Chlorflavonin and Analogs via a Late-Stage Ruthenium(II)-Catalyzed ortho-C(sp2)-H-Hydroxylation

The continuous, worldwide spread of multidrug-resistant (MDR) and extensively drug-resistant (XDR) tuberculosis (TB) endanger the World Health Organization’s (WHO) goal to end the global TB pandemic by the year 2035. During the past 50 years, very few new drugs have been approved by medical agencies to treat drug-resistant TB. Therefore, the development of novel antimycobacterial drug candidates to combat the threat of drug-resistant TB is urgent. In this work, we developed and optimized a total synthesis of the antimycobacterial natural flavonoid chlorflavonin by selective ruthenium(II)-catalyzed ortho-C(sp²)-H-hydroxylation of a substituted 3′-methoxyflavonoid skeleton. We extended our methodology to synthesize a small compound library of 14 structural analogs. The new analogs were tested for their antimycobacterial in vitro activity against Mycobacterium tuberculosis (Mtb) and their cytotoxicity against various human cell lines. The most promising new analog bromflavonin exhibited improved antimycobacterial in vitro activity against the virulent H37Rv strain of Mtb (Minimal Inhibitory Concentrations (MIC₉₀) = 0.78 μm). In addition, we determined the chemical and metabolic stability as well as the pK_a values of chlorflavonin and bromflavonin. Furthermore, we established a quantitative structure–activity relationship model using a thermodynamic integration approach. Our computations may be used for suggesting further structural changes to develop improved derivatives.

Drug Discovery by Automated Adaptation of Chemical Structure and Identity

Computer-aided drug design offers the potential to dramatically reduce the cost and effort required for drug discovery. While screening-based methods are valuable in the early stages of hit identification, they are frequently succeeded by iterative, hypothesis-driven computations that require recurrent investment of human time and intuition. To increase automation, we introduce a computational method for lead refinement that combines concerted dynamics of the ligand/protein complex via molecular dynamics simulations with integrated Monte Carlo-based changes in the chemical formula of the ligand. This approach, which we refer to as ligand-exchange Monte Carlo molecular dynamics, accounts for solvent- and entropy-based contributions to competitive binding free energies by coupling the energetics of bound and unbound states during the ligand-exchange attempt. Quantitative comparison of relative binding free energies to reference values from free energy perturbation, conducted in vacuum, indicates that ligand-exchange Monte Carlo molecular dynamics simulations sample relevant conformational ensembles and are capable of identifying strongly binding compounds. Additional simulations demonstrate the use of an implicit solvent model. We speculate that the use of chemical graphs in which exchanges are only permitted between ligands with sufficient similarity may enable an automated search to capture some of the benefits provided by human intuition during hypothesis-guided lead refinement.

Fisetin 8-C-glucoside as entry inhibitor in SARS CoV-2 infection: molecular modelling study

Coronaviruses are RNA viruses that infect varied species including humans. TMPRSS2 is gateway for SARS CoV-2 entry into the host cell. It causes proteolytic activation of spike protein and discharge of the peptide into host cell. The TMPRSS2 inhibition could be one of the approaches to stop the viral entry, therefore, interaction pattern and binding energies for Fisetin and TMPRSS2 have been explored in the present study. TMPRSS2 peptide was used for homology modelling and then for further study. Molecular docking score and MMGBSA Binding energy of Fisetin was better than Nafamostat, a known inhibitor of TMPRSS2. Post docking MM-GBSA free energy for Fisetin and Nafamostat was -42.78 and -21.11 kcal/mol, respectively. Fisetin forms H bond with Val 25, His 41, Lys 42, Lys 45, Glu 44, Ser186. Nafamostat formed H bonds with Lys 85, Asp 90, Asp 203. RMSD plots of TMPRSS2, TMPRSS2-Fisetin and TMPRSS2-Nafamostat complex showed stable profile with very small fluctuation during entire simulation of 150 ns. Significant decrease in TMPRSS2-Fisetin and TMPRSS2-Nafamostat complex fluctuation occurred around His 41, Glu 44, Gly 136, Ser 186 in RMSF study. During simulation Fisetin interaction was observed with residues Val 25, His 41, Glu 44, Lys 45, Lys 87, Gly 136, Gln 183, Ser 186 likewise interaction of Nafamostat with Lys 85, Asp 90, Asn 163, Asp 203 and Ser 205. Post simulation MM-GBSA free energy was found to be -51.87 ± 4.3 and -48.23 ± 4.39 kcal/mol for TMPRSS2 with Fisetin and Nafamostat, respectively.Communicated by Ramaswamy H. Sarma.

Modified Hamiltonian in FEP Calculations for Reducing the Computational Cost of Electrostatic Interactions

The free-energy perturbation (FEP) method predicts relative and absolute free-energy changes of biomolecules in solvation and binding with other molecules. FEP is, therefore, one of the most essential tools in in silico drug design. In conventional FEP, to smoothly connect two thermodynamic states, the potential energy is modified as a linear combination of the end-state potential energies by introducing scaling factors. When the particle mesh Ewald is used for electrostatic calculations, conventional FEP requires two reciprocal-space calculations per time step, which largely decreases the computational performance. To overcome this problem, we propose a new FEP scheme by introducing a modified Hamiltonian instead of interpolation of the end-state potential energies. The scheme introduces nonuniform scaling into the electrostatic potential as used in Replica Exchange with Solute Tempering 2 (REST2) and does not require additional reciprocal-space calculations. We tested this modified Hamiltonian in FEP calculations in several biomolecular systems. In all cases, the calculated free-energy changes with the current scheme are in good agreement with those from conventional FEP. The modified Hamiltonian in FEP greatly improves the computational performance, which is particularly marked for large biomolecular systems whose reciprocal-space calculations are the major bottleneck of total computational time.

Computational Design and Biological Evaluation of Analogs of Lupin Peptide P5 Endowed with Dual PCSK9/HMG-CoAR Inhibiting Activity

(1) Background: Proprotein convertase subtilisin/kexin 9 (PCSK9) is responsible for the degradation of the hepatic low-density lipoprotein receptor (LDLR), which regulates the circulating cholesterol level. In this field, we discovered natural peptides derived from lupin that showed PCSK9 inhibitory activity. Among these, the most active peptide, known as P5 (LILPHKSDAD), reduced the protein-protein interaction between PCSK9 and LDLR with an IC₅₀ equals to 1.6 µM and showed a dual hypocholesterolemic activity, since it shows complementary inhibition of the 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMG-CoAR). (2) Methods: In this study, by a computational approach, the P5 primary structure was optimized to obtain new analogs with improved affinity to PCSK9. Then, biological assays were carried out for fully characterizing the dual cholesterol-lowering activity of the P5 analogs by using both biochemical and cellular techniques. (3) Results: A new peptide, P5-Best (LYLPKHSDRD) displayed improved PCSK9 (IC₅₀ 0.7 µM) and HMG-CoAR (IC₅₀ 88.9 µM) inhibitory activities. Moreover, in vitro biological assays on cells demonstrated that, not only P5-Best, but all tested peptides maintained the dual PCSK9/HMG-CoAR inhibitory activity and remarkably P5-Best exerted the strongest hypocholesterolemic effect. In fact, in the presence of this peptide, the ability of HepG2 cells to absorb extracellular LDL was improved by up to 254%. (4) Conclusions: the atomistic details of the P5-Best/PCSK9 and P5-Best/HMG-CoAR interactions represent a reliable starting point for the design of new promising molecular entities endowed with hypocholesterolemic activity.

Progress in Free Energy Perturbation: Options for Evolving Fragments

One of the remaining bottlenecks in fragment-based drug design (FBDD) is the initial exploration and optimization of the identified hit fragments. There is a growing interest in computational approaches that can guide these efforts by predicting the binding affinity of newly designed analogues. Among others, alchemical free energy (AFE) calculations promise high accuracy at a computational cost that allows their application during lead optimization campaigns. In this review, we discuss how AFE could have a strong impact in fragment evolution, and we raise awareness on the challenges that could be encountered applying this methodology in FBDD studies.

Searching and designing potential inhibitors for SARS-CoV-2 Mpro from natural sources using atomistic and deep-learning calculations

The spread of severe acute respiratory syndrome coronavirus 2 novel coronavirus (SARS-CoV-2) worldwide has caused the coronavirus disease 2019 (COVID-19) pandemic. A hundred million people were infected, resulting in several millions of death worldwide. In order to prevent viral replication, scientists have been aiming to prevent the biological activity of the SARS-CoV-2 main protease (3CL pro or Mpro). In this work, we demonstrate that using a reasonable combination of deep-learning calculations and atomistic simulations could lead to a new approach for developing SARS-CoV-2 main protease (Mpro) inhibitors. Initially, the binding affinities of the natural compounds to SARS-CoV-2 Mpro were estimated via atomistic simulations. The compound tomatine, thevetine, and tribuloside could bind to SARS-CoV-2 Mpro with nanomolar/high-nanomolar affinities. Secondly, the deep-learning (DL) calculations were performed to chemically alter the top-lead natural compounds to improve ligand-binding affinity. The obtained results were then validated by free energy calculations using atomistic simulations. The outcome of the research will probably boost COVID-19 therapy.

Substrate Access Mechanism in a Novel Membrane-Bound Phospholipase A of Pseudomonas aeruginosa Concordant with Specificity and Regioselectivity

PlaF is a cytoplasmic membrane-bound phospholipase A₁ from Pseudomonas aeruginosa that alters the membrane glycerophospholipid (GPL) composition and fosters the virulence of this human pathogen. PlaF activity is regulated by a dimer-to-monomer transition followed by tilting of the monomer in the membrane. However, how substrates reach the active site and how the characteristics of the active site tunnels determine the activity, specificity, and regioselectivity of PlaF for natural GPL substrates have remained elusive. Here, we combined unbiased and biased all-atom molecular dynamics (MD) simulations and configurational free-energy computations to identify access pathways of GPL substrates to the catalytic center of PlaF. Our results map out a distinct tunnel through which substrates access the catalytic center. PlaF variants with bulky tryptophan residues in this tunnel revealed decreased catalysis rates due to tunnel blockage. The MD simulations suggest that GPLs preferably enter the active site with the sn-1 acyl chain first, which agrees with the experimentally demonstrated PLA₁ activity of PlaF. We propose that the acyl chain-length specificity of PlaF is determined by the structural features of the access tunnel, which results in favorable free energy of binding of medium-chain GPLs. The suggested egress route conveys fatty acid (FA) products to the dimerization interface and, thus, contributes to understanding the product feedback regulation of PlaF by FA-triggered dimerization. These findings open up opportunities for developing potential PlaF inhibitors, which may act as antibiotics against P. aeruginosa.

Machine Learning Applied to the Modeling of Pharmacological and ADMET Endpoints

The well-known concept of quantitative structure-activity relationships (QSAR) has been gaining significant interest in the recent years. Data, descriptors, and algorithms are the main pillars to build useful models that support more efficient drug discovery processes with in silico methods. Significant advances in all three areas are the reason for the regained interest in these models. In this book chapter we review various machine learning (ML) approaches that make use of measured in vitro/in vivo data of many compounds. We put these in context with other digital drug discovery methods and present some application examples.

Molecular design of anticancer drugs from marine fungi derivatives

Heat shock protein 90 (Hsp90) is one of the most potential targets in cancer therapy. We have demonstrated using a combination of molecular docking and fast pulling of ligand (FPL) simulations that marine fungi derivatives can be possible inhibitors, preventing the biological activity of Hsp90. The computational approaches were validated and compared with previous experiments. Based on the benchmark of available inhibitors of Hsp90, the GOLD docking package using the ChemPLP scoring function was found to be superior over both Autodock Vina and Autodock4 in the preliminary estimation of the ligand-binding affinity and binding pose with the Pearson correlation, R = -0.62. Moreover, FPL calculations were also indicated as a suitable approach to refine docking simulations with a correlation coefficient with the experimental data of R = -0.81. Therefore, the binding affinity of marine fungi derivatives to Hsp90 was evaluated. Docking and FPL calculations suggest that five compounds including 23, 40, 46, 48, and 52 are highly potent inhibitors for Hsp90. The obtained results enhance cancer therapy research.

Benchmark of Popular Free Energy Approaches Revealing the Inhibitors Binding to SARS-CoV-2 Mpro

The COVID-19 pandemic has killed millions of people worldwide since its outbreak in December 2019. The pandemic is caused by the SARS-CoV-2 virus whose main protease (Mpro) is a promising drug target since it plays a key role in viral proliferation and replication. Currently, developing an effective therapy is an urgent task, which requires accurately estimating the ligand-binding free energy to SARS-CoV-2 Mpro. However, it should be noted that the accuracy of a free energy method probably depends on the protein target. A highly accurate approach for some targets may fail to produce a reasonable correlation with the experiment when a novel enzyme is considered as a drug target. Therefore, in this context, the ligand-binding affinity to SARS-CoV-2 Mpro was calculated via various approaches. The molecular docking approach was manipulated using Autodock Vina (Vina) and Autodock4 (AD4) protocols to preliminarily investigate the ligand-binding affinity and pose to SARS-CoV-2 Mpro. The binding free energy was then refined using the fast pulling of ligand (FPL), linear interaction energy (LIE), molecular mechanics-Poisson-Boltzmann surface area (MM-PBSA), and free energy perturbation (FEP) methods. The benchmark results indicated that for docking calculations, Vina is more accurate than AD4, and for free energy methods, FEP is the most accurate method, followed by LIE, FPL, and MM-PBSA (FEP > LIE ≈ FPL > MM-PBSA). Moreover, atomistic simulations revealed that the van der Waals interaction is the dominant factor. The residues Thr26, His41, Ser46, Asn142, Gly143, Cys145, His164, Glu166, and Gln189 are essential elements affecting the binding process. Our benchmark provides guidelines for further investigations using computational approaches.

New isolate from Salvinia molesta with antioxidant and urease inhibitory activity

Urease plays a significant role in the pathogenesis of urolithiasis pyelonephritis, urinary catheter encrustation, hepatic coma, hepatic encephalopathy, and peptic acid duodenal ulcers. Salvinia molesta was explored to identify new bioactive compounds with particular emphasis on urease inhibitors. The aqueous methanol extract was fractionated using solvents of increasing polarity. A series of column chromatography and later HPLC were performed on butanol extract. The structures of the resulting pure compounds were resolved using NMR (1D and 2D), infrared, and mass spectroscopy. The novel isolate was evaluated for antioxidant activity (using DPPH, superoxide anion radical scavenging, oxidative burst, and Fe⁺² chelation assays), anti-glycation behavior, anticancer activity, carbonic anhydrase inhibition, phosphodiesterase inhibition, and urease inhibition. One new glucopyranose derivative 6'-O-(3,4-dihydroxybenzoyl)-4'-O-(4-hydroxybenzoyl)-α/β-D-glucopyranoside (1) and four known glycosides were identified. Glycoside 1 demonstrated promising antioxidant potential with IC₅₀ values of 48.2 ± 0.3, 60.3 ± 0.6, and 42.1 ± 1.8 μM against DPPH, superoxide radical, and oxidative burst, respectively. Its IC₅₀ in the Jack bean urease inhibition assay was 99.1 ± 0.8 μM. The mechanism-based kinetic studies presented that compound 1 is a mixed-type inhibitor of urease with a K_i value of 91.8 ± 0.1 μM. Finally, molecular dynamic simulations exploring the binding mode of compound 1 with urease provided quantitative agreement between estimated binding free energies and the experimental results. The studies corroborate the use of compound 1 as a lead for QSAR studies as an antioxidant and urease inhibitor. Moreover, it needs to be further evaluated through the animal model, that is, in vivo or tissue culture-based ex-vivo studies, to establish their therapeutic potential against oxidative stress phosphodiesterase-II and urease-induced pathologies.

Computational estimation of potential inhibitors from known drugs against the main protease of SARS-CoV-2

The coronavirus disease (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rapidly spread worldwide recently, leading to global social and economic disruption. Although the emergently approved vaccine programs against SARS-CoV-2 have been rolled out globally, the number of COVID-19 daily cases and deaths has remained significantly high. Here, we attempt to computationally screen for possible medications for COVID-19 via rapidly estimating the highly potential inhibitors from an FDA-approved drug database against the main protease (Mpro) of SARS-CoV-2. The approach combined molecular docking and fast pulling of ligand (FPL) simulations that were demonstrated to be accurate and suitable for quick prediction of SARS-CoV-2 Mpro inhibitors. The results suggested that twenty-seven compounds were capable of strongly associating with SARS-CoV-2 Mpro. Among them, the seven top leads are daclatasvir, teniposide, etoposide, levoleucovorin, naldemedine, cabozantinib, and irinotecan. The potential application of these drugs in COVID-19 therapy has thus been discussed.

An Effective MM/GBSA Protocol for Absolute Binding Free Energy Calculations: A Case Study on SARS-CoV-2 Spike Protein and the Human ACE2 Receptor

The binding free energy calculation of protein-ligand complexes is necessary for research into virus-host interactions and the relevant applications in drug discovery. However, many current computational methods of such calculations are either inefficient or inaccurate in practice. Utilizing implicit solvent models in the molecular mechanics generalized Born surface area (MM/GBSA) framework allows for efficient calculations without significant loss of accuracy. Here, GBNSR6, a new flavor of the generalized Born model, is employed in the MM/GBSA framework for measuring the binding affinity between SARS-CoV-2 spike protein and the human ACE2 receptor. A computational protocol is developed based on the widely studied Ras-Raf complex, which has similar binding free energy to SARS-CoV-2/ACE2. Two options for representing the dielectric boundary of the complexes are evaluated: one based on the standard Bondi radii and the other based on a newly developed set of atomic radii (OPT1), optimized specifically for protein-ligand binding. Predictions based on the two radii sets provide upper and lower bounds on the experimental references: -14.7(ΔGbindBondi)<-10.6(ΔGbindExp.)<-4.1(ΔGbindOPT1) kcal/mol. The consensus estimates of the two bounds show quantitative agreement with the experiment values. This work also presents a novel truncation method and computational strategies for efficient entropy calculations with normal mode analysis. Interestingly, it is observed that a significant decrease in the number of snapshots does not affect the accuracy of entropy calculation, while it does lower computation time appreciably. The proposed MM/GBSA protocol can be used to study the binding mechanism of new variants of SARS-CoV-2, as well as other relevant structures.

Machine learning-accelerated quantum mechanics-based atomistic simulations for industrial applications

Atomistic simulations have become an invaluable tool for industrial applications ranging from the optimization of protein-ligand interactions for drug discovery to the design of new materials for energy applications. Here we review recent advances in the use of machine learning (ML) methods for accelerated simulations based on a quantum mechanical (QM) description of the system. We show how recent progress in ML methods has dramatically extended the applicability range of conventional QM-based simulations, allowing to calculate industrially relevant properties with enhanced accuracy, at reduced computational cost, and for length and time scales that would have otherwise not been accessible. We illustrate the benefits of ML-accelerated atomistic simulations for industrial R&D processes by showcasing relevant applications from two very different areas, drug discovery (pharmaceuticals) and energy materials. Writing from the perspective of both a molecular and a materials modeling scientist, this review aims to provide a unified picture of the impact of ML-accelerated atomistic simulations on the pharmaceutical, chemical, and materials industries and gives an outlook on the exciting opportunities that could emerge in the future.

Accurate Receptor-Ligand Binding Free Energies from Fast QM Conformational Chemical Space Sampling

Small molecule receptor-binding is dominated by weak, non-covalent interactions such as van-der-Waals hydrogen bonding or electrostatics. Calculating these non-covalent ligand-receptor interactions is a challenge to computational means in terms of accuracy and efficacy since the ligand may bind in a number of thermally accessible conformations. The conformational rotamer ensemble sampling tool (CREST) uses an iterative scheme to efficiently sample the conformational space and calculates energies using the semi-empirical 'Geometry, Frequency, Noncovalent, eXtended Tight Binding' (GFN2-xTB) method. This combined approach is applied to blind predictions of the modes and free energies of binding for a set of 10 drug molecule ligands to the cucurbit[n]urils CB[8] receptor from the recent 'Statistical Assessment of the Modeling of Proteins and Ligands' (SAMPL) challenge including morphine, hydromorphine, cocaine, fentanyl, and ketamine. For each system, the conformational space was sufficiently sampled for the free ligand and the ligand-receptor complexes using the quantum chemical Hamiltonian. A multitude of structures makes up the final conformer-rotamer ensemble, for which then free energies of binding are calculated. For those large and complex molecules, the results are in good agreement with experimental values with a mean error of 3 kcal/mol. The GFN2-xTB energies of binding are validated by advanced density functional theory calculations and found to be in good agreement. The efficacy of the automated QM sampling workflow allows the extension towards other complex molecular interaction scenarios.

Evaluation of binding of potential ADMET/tox screened saquinavir analogues for inhibition of HIV-protease via molecular dynamics and binding free energy calculations

Developing novel drug molecules against HIV is a scientific quest necessitated by development of drug resistance against used drugs. We report comparative results of molecular dynamics simulation studies on 11 structural analogues of Saquinavir (SQV) - against HIV-protease that were earlier examined for pharmacodynamic and pharmacokinetic properties. We reported analogues S1, S5 and S8 to qualify the ADMET criterion and may be considered as potential lead molecules. In this study the designed molecules were successively docked with native HIV-protease at AutoDock. Docking scores established relative goodness of the 11 analogues against the benchmark for Saquinavir. The docked complexes were subjected to molecular dynamics simulation studies using GROMACS 4.6.2. Four parameters viz. H-bonding, RMSD, Binding energy and Protein-Ligand Distance were used for comparative analyses of the analogues relative to Saquinavir. The comparison and analysis of the results are indicative that analogues S8, S9 and S1 are promising candidates among all the analogues studied. From our earlier work and present study it is evident that among the three S8 and S1 qualify the ADMET criterion and between S1 and S8, the analogue S8 shows more target efficacy and specificity over S1 and have better molecular dynamics simulation results. Thus, of the 11 de novo Saquinavir analogues, the S8 appears to be the most promising candidate as lead molecule for HIV-protease inhibitor and is best suited for testing under biological system. Further validation of the proposed lead molecules through wet lab studies involving antiviral assays however is required.Communicated by Ramaswamy H. Sarma.

Binding of inhibitors to the monomeric and dimeric SARS-CoV-2 Mpro

SARS-CoV-2 rapidly infects millions of people worldwide since December 2019. There is still no effective treatment for the virus, resulting in the death of more than one million patients. Inhibiting the activity of SARS-CoV-2 main protease (Mpro), 3C-like protease (3CLP), is able to block the viral replication and proliferation. In this context, our study has revealed that in silico screening for inhibitors of SARS-CoV-2 Mpro can be reliably done using the monomeric structure of the Mpro instead of the dimeric one. Docking and fast pulling of ligand (FPL) simulations for both monomeric and dimeric forms correlate well with the corresponding experimental binding affinity data of 24 compounds. The obtained results were also confirmed via binding pose and noncovalent contact analyses. Our study results show that it is possible to speed up computer-aided drug design for SARS-CoV-2 Mpro by focusing on the monomeric form instead of the larger dimeric one.

Computational Determination of Potential Inhibitors of SARS-CoV-2 Main Protease

The novel coronavirus (SARS-CoV-2) has infected several million people and caused thousands of deaths worldwide since December 2019. As the disease is spreading rapidly all over the world, it is urgent to find effective drugs to treat the virus. The main protease (Mpro) of SARS-CoV-2 is one of the potential drug targets. Therefore, in this context, we used rigorous computational methods, including molecular docking, fast pulling of ligand (FPL), and free energy perturbation (FEP), to investigate potential inhibitors of SARS-CoV-2 Mpro. We first tested our approach with three reported inhibitors of SARS-CoV-2 Mpro, and our computational results are in good agreement with the respective experimental data. Subsequently, we applied our approach on a database of ∼4600 natural compounds, as well as 8 available HIV-1 protease (PR) inhibitors and an aza-peptide epoxide. Molecular docking resulted in a short list of 35 natural compounds, which was subsequently refined using the FPL scheme. FPL simulations resulted in five potential inhibitors, including three natural compounds and two available HIV-1 PR inhibitors. Finally, FEP, the most accurate and precise method, was used to determine the absolute binding free energy of these five compounds. FEP results indicate that two natural compounds, cannabisin A and isoacteoside, and an HIV-1 PR inhibitor, darunavir, exhibit a large binding free energy to SARS-CoV-2 Mpro, which is larger than that of 13b, the most reliable SARS-CoV-2 Mpro inhibitor recently reported. The binding free energy largely arises from van der Waals interaction. We also found that Glu166 forms H-bonds to all of the inhibitors. Replacing Glu166 by an alanine residue leads to ∼2.0 kcal/mol decreases in the affinity of darunavir to SARS-CoV-2 Mpro. Our results could contribute to the development of potential drugs inhibiting SARS-CoV-2.

Rapid, accurate, precise and reproducible ligand-protein binding free energy prediction

A central quantity of interest in molecular biology and medicine is the free energy of binding of a molecule to a target biomacromolecule. Until recently, the accurate prediction of binding affinity had been widely regarded as out of reach of theoretical methods owing to the lack of reproducibility of the available methods, not to mention their complexity, computational cost and time-consuming procedures. The lack of reproducibility stems primarily from the chaotic nature of classical molecular dynamics (MD) and the associated extreme sensitivity of trajectories to their initial conditions. Here, we review computational approaches for both relative and absolute binding free energy calculations, and illustrate their application to a diverse set of ligands bound to a range of proteins with immediate relevance in a number of medical domains. We focus on ensemble-based methods which are essential in order to compute statistically robust results, including two we have recently developed, namely thermodynamic integration with enhanced sampling and enhanced sampling of MD with an approximation of continuum solvent. Together, these form a set of rapid, accurate, precise and reproducible free energy methods. They can be used in real-world problems such as hit-to-lead and lead optimization stages in drug discovery, and in personalized medicine. These applications show that individual binding affinities equipped with uncertainty quantification may be computed in a few hours on a massive scale given access to suitable high-end computing resources and workflow automation. A high level of accuracy can be achieved using these approaches.

Alchemical Binding Free Energy Calculations in AMBER20: Advances and Best Practices for Drug Discovery

Predicting protein-ligand binding affinities and the associated thermodynamics of biomolecular recognition is a primary objective of structure-based drug design. Alchemical free energy simulations offer a highly accurate and computationally efficient route to achieving this goal. While the AMBER molecular dynamics package has successfully been used for alchemical free energy simulations in academic research groups for decades, widespread impact in industrial drug discovery settings has been minimal because of the previous limitations within the AMBER alchemical code, coupled with challenges in system setup and postprocessing workflows. Through a close academia-industry collaboration we have addressed many of the previous limitations with an aim to improve accuracy, efficiency, and robustness of alchemical binding free energy simulations in industrial drug discovery applications. Here, we highlight some of the recent advances in AMBER20 with a focus on alchemical binding free energy (BFE) calculations, which are less computationally intensive than alternative binding free energy methods where full binding/unbinding paths are explored. In addition to scientific and technical advances in AMBER20, we also describe the essential practical aspects associated with running relative alchemical BFE calculations, along with recommendations for best practices, highlighting the importance not only of the alchemical simulation code but also the auxiliary functionalities and expertise required to obtain accurate and reliable results. This work is intended to provide a contemporary overview of the scientific, technical, and practical issues associated with running relative BFE simulations in AMBER20, with a focus on real-world drug discovery applications.

A second shell residue modulates a conserved ATP-binding site with radically different affinities for ATP

BACKGROUND

Prediction of ligand binding and design of new function in enzymes is a time-consuming and expensive process. Crystallography gives the impression that proteins adopt a fixed shape, yet enzymes are functionally dynamic. Molecular dynamics offers the possibility of probing protein movement while predicting ligand binding. Accordingly, we choose the bacterial F₁F_o ATP synthase ε subunit to unravel why ATP affinity by ε subunits from Bacillus subtilis and Bacillus PS3 differs ~500-fold, despite sharing identical sequences at the ATP-binding site.

METHODS

We first used the Bacillus PS3 ε subunit structure to model the B. subtilis ε subunit structure and used this to explore the utility of molecular dynamics (MD) simulations to predict the influence of residues outside the ATP binding site. To verify the MD predictions, point mutants were made and ATP binding studies were employed.

RESULTS

MD simulations predicted that E102 in the B. subtilis ε subunit, outside of the ATP binding site, influences ATP binding affinity. Engineering E102 to alanine or arginine revealed a ~10 or ~54 fold increase in ATP binding, respectively, confirming the MD prediction that E102 drastically influences ATP binding affinity.

CONCLUSIONS

These findings reveal how MD can predict how changes in the "second shell" residues around substrate binding sites influence affinity in simple protein structures. Our results reveal why seemingly identical ε subunits in different ATP synthases have radically different ATP binding affinities.

GENERAL SIGNIFICANCE

This study may lead to greater utility of molecular dynamics as a tool for protein design and exploration of protein design and function.