Relative genotoxicity of polycyclic aromatic hydrocarbons inferred from free energy perturbation approaches.

Utilizing molecular dynamics and free energy perturbation, we examine the relative binding affinity of several covalent polycyclic aromatic hydrocarbon - DNA (PAH-DNA) adducts at the central adenine of NRAS codon-61, a mutational hotspot implicated in cancer risk. Several PAHs classified by the International Agency for Research on Cancer as probable, possible, or unclassifiable as to carcinogenicity are found to have greater binding affinity than the known carcinogen, benzo[a]pyrene (B[a]P). van der Waals interactions between the intercalated PAH and neighboring nucleobases, and minimal disruption of the DNA duplex drive increases in binding affinity. PAH-DNA adducts may be repaired by global genomic nucleotide excision repair (GG-NER), hence we also compute relative free energies of complexation of PAH-DNA adducts with RAD4-RAD23 (the yeast ortholog of human XPC-RAD23) which constitutes the recognition step in GG-NER. PAH-DNA adducts exhibiting the greatest DNA binding affinity also exhibit the least RAD4-RAD23 complexation affinity and are thus predicted to resist the GG-NER machinery, contributing to their genotoxic potential. In particular, the fjord region PAHs dibenzo[a,l]pyrene, benzo[g]chrysene, and benzo[c]phenanthrene are found to have greater binding affinity while having weaker RAD4-RAD23 complexation affinity than their respective bay region analogs B[a]P, chrysene, and phenanthrene. We also find that the bay region PAHs dibenzo[a,j]anthracene, dibenzo[a,c]anthracene, and dibenzo[a,h]anthracene exhibit greater binding affinity and weaker RAD4-RAD23 complexation affinity than B[a]P. Thus, the study of PAH genotoxicity likely needs to be substantially broadened, with implications for public policy and the health sciences. This approach can be broadly applied to assess factors contributing to the genotoxicity of other unclassified compounds.

Dynamic extracellular vestibule of human SERT: Unveiling druggable potential with high-affinity allosteric inhibitors.

The serotonin transporter (SERT) tightly regulates synaptic serotonin levels and has been the primary target of antidepressants. Binding of inhibitors to the allosteric site of human SERT (hSERT) impedes the dissociation of antidepressants bound at the central site and may enhance the efficacy of such antidepressants to potentially reduce their dosage and side effects. Here, we report the identification of a series of high-affinity allosteric inhibitors of hSERT in a unique scaffold, with the lead compound, Lu AF88273 (3-(1-(2-(1H-indol-3-yl)ethyl)piperidin-4-yl)-6-chloro-1H-indole), having 2.1 nM allosteric potency in inhibiting imipramine dissociation. In addition, we find that Lu AF88273 also inhibits serotonin transport in a noncompetitive manner. The binding pose of Lu AF88273 in the allosteric site of hSERT is determined with extensive molecular dynamics simulations and rigorous absolute binding free energy perturbation (FEP) calculations, which show that a part of the compound occupies a dynamically formed small cavity. The predicted binding location and pose are validated by site-directed mutagenesis and can explain much of the structure-activity relationship of these inhibitors using the relative binding FEP calculations. Together, our findings provide a promising lead compound and the structural basis for the development of allosteric drugs targeting hSERT. Further, they demonstrate that the divergent allosteric sites of neurotransmitter transporters can be selectively targeted.

A mutagenesis study of autoantigen optimization for potential T1D vaccine design.

A previously reported autoreactive antigen, termed the X-idiotype, isolated from a unique cell population in Type 1 diabetes (T1D) patients, was found to stimulate their CD4+ T cells. This antigen was previously determined to bind more favorably than insulin and its mimic (insulin superagonist) to HLA-DQ8, supporting its strong role in CD4+ T cell activation. In this work, we probed HLA-X-idiotype-TCR binding and designed enhanced-reactive pHLA-TCR antigens using an in silico mutagenesis approach which we functionally validated by cell proliferation assays and flow cytometry. From a combination of single, double, and swap mutations, we identified antigen-binding sites p4 and p6 as potential mutation sites for HLA binding affinity enhancement. Site p6 is revealed to favor smaller but more hydrophobic residues than the native tyrosine, such as valine (Y6V) and isoleucine (Y6I), indicating a steric mechanism in binding affinity improvement. Meanwhile, site p4 methionine mutation to hydrophobic residues isoleucine (M4I) or leucine (M4L) modestly increases HLA binding affinity. Select p6 mutations to cysteine (Y6C) or isoleucine (Y6I) exhibit favorable TCR binding affinities, while a swap p5-p6 tyrosine-valine double mutant (V5Y_Y6V) and a p6-p7 glutamine-glutamine double mutant (Y6Q_Y7Q) exhibit enhanced HLA binding affinity but weakened TCR affinity. This work holds relevance to potential T1D antigen-based vaccine design and optimization.

Specific protonation of acidic residues confers K+ selectivity to the gastric proton pump.

The gastric proton pump (H+,K+-ATPase) transports a proton into the stomach lumen for every K+ ion exchanged in the opposite direction. In the lumen-facing state of the pump (E2), the pump selectively binds K+ despite the presence of a 10-fold higher concentration of Na+. The molecular basis for the ion selectivity of the pump is unknown. Using molecular dynamics simulations, free energy calculations, and Na+ and K+-dependent ATPase activity assays, we demonstrate that the K+ selectivity of the pump depends upon the simultaneous protonation of the acidic residues E343 and E795 in the ion-binding site. We also show that when E936 is protonated, the pump becomes Na+ sensitive. The protonation-mimetic mutant E936Q exhibits weak Na+-activated ATPase activity. A 2.5-Å resolution cryo-EM structure of the E936Q mutant in the K+-occluded E2-Pi form shows, however, no significant structural difference compared with wildtype except less-than-ideal coordination of K+ in the mutant. The selectivity toward a specific ion correlates with a more rigid and less fluctuating ion-binding site. Despite being exposed to a pH of 1, the fundamental principle driving the K+ ion selectivity of H+,K+-ATPase is similar to that of Na+,K+-ATPase: the ionization states of the acidic residues in the ion-binding sites determine ion selectivity. Unlike the Na+,K+-ATPase, however, protonation of an ion-binding glutamate residue (E936) confers Na+ sensitivity.

Exploring ligands that target von Willebrand factor selectively under oxidizing conditions through docking and molecular dynamics simulations.

The blood protein von Willebrand factor (VWF) is a large multimeric protein that, when activated, binds to blood platelets, tethering them to the site of vascular injury and initiating blood coagulation. This process is critical for the normal hemostatic response, but especially under inflammatory conditions, it is thought to be a major player in pathological thrombus formation. For this reason, VWF has been the target for the development of anti-thrombotic therapeutics. However, it is challenging to prevent pathological thrombus formation while still allowing normal physiological blood coagulation, as currently available anti-thrombotic therapeutics are known to cause unwanted bleeding, in particular intracranial hemorrhage. This work explores the possibility of inhibiting VWF selectively under the inflammatory conditions present during pathological thrombus formation. In particular, the A2 domain of VWF is known to inhibit the neighboring A1 domain from binding to the platelet surface receptor GpIbα, and this auto-inhibitory mechanism has been shown to be removed by oxidizing agents released during inflammation. Hence, finding drug molecules that bind at the interface between A1 and A2 only under oxidizing conditions could restore such an auto-inhibitory mechanism. Here, by using a combination of computational docking, molecular dynamics simulations, and free energy perturbation calculations, a ligand from the ZINC15 database was identified that binds at the A1A2 interface, with the interaction being stronger under oxidizing conditions. The results provide a framework for the discovery of drug molecules that bind to a protein selectively in the presence of inflammatory conditions.

Adaptive lambda schemes for efficient relative binding free energy calculation.

The relative free energy perturbation (RFEP) calculation is one of the most theoretically sound computational chemistry approaches for the binding affinity prediction. However, its application is often hindered by the complexity of the calculation choices and the requirement of expertise in the field. Improper lambda scheme of RFEP may result in deviations from an accurate description of the perturbation process and is prone to erroneous affinity predictions. To address such challenges, an automated adaptive lambda method is proposed where the adaptive lambda schemes are obtained through a split-and-merge algorithm based on the pilot runs. The newly established workflow along with a series of improvements to the perturbation settings increases the consistency of the RFEP calculation results. Comparing the pilot and adaptive lambda schemes, the latter demonstrated improvements in convergence and reproducibility and lowered the mean unsigned error and the root-mean-square error. Overall, the adaptive lambda method is a reliable and robust choice to predict small molecule relative binding free energy and can be capitalized to benefit routine RFEP calculations for drug discovery projects.

Exploiting high-energy hydration sites for the discovery of potent peptide aldehyde inhibitors of the SARS-CoV-2 main protease with cellular antiviral activity.

Small-molecule antivirals that prevent the replication of the SARS-CoV-2 virus by blocking the enzymatic activity of its main protease (Mpro) are and will be a tenet of pandemic preparedness. However, the peptidic nature of such compounds often precludes the design of compounds within favorable physical property ranges, limiting cellular activity. Here we describe the discovery of peptide aldehyde Mpro inhibitors with potent enzymatic and cellular antiviral activity. This structure-activity relationship (SAR) exploration was guided by the use of calculated hydration site thermodynamic maps (WaterMap) to drive potency via displacement of waters from high-energy sites. Thousands of diverse compounds were designed to target these high-energy hydration sites and then prioritized for synthesis by physics- and structure-based Free-Energy Perturbation (FEP+) simulations, which accurately predicted biochemical potencies. This approach ultimately led to the rapid discovery of lead compounds with unique SAR that exhibited potent enzymatic and cellular activity with excellent pan-coronavirus coverage.

The slow but steady rise of binding free energy calculations in drug discovery.

Binding free energy calculations are increasingly used in drug discovery research to predict protein-ligand binding affinities and to prioritize candidate drug molecules accordingly. It has taken decades of collective effort to transform this academic concept into a technology adopted by the pharmaceutical and biotech industry. Having personally witnessed and taken part in this transformation, here I recount the (incomplete) list of problems that had to be solved to make this computational tool practical and suggest areas of future development.

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 (ΔGbind) 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 ΔGbind. 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.

Searching for potential inhibitors of SARS-COV-2 main protease using supervised learning and perturbation calculations.

Inhibiting the biological activity of SARS-CoV-2 Mpro can prevent viral replication. In this context, a hybrid approach using knowledge- and physics-based methods was proposed to characterize potential inhibitors for SARS-CoV-2 Mpro. Initially, supervised machine learning (ML) models were trained to predict a ligand-binding affinity of ca. 2 million compounds with the correlation on a test set of R = 0.748 ± 0.044 . Atomistic simulations were then used to refine the outcome of the ML model. Using LIE/FEP calculations, nine compounds from the top 100 ML inhibitors were suggested to bind well to the protease with the domination of van der Waals interactions. Furthermore, the binding affinity of these compounds is also higher than that of nirmatrelvir, which was recently approved by the US FDA to treat COVID-19. In addition, the ligands altered the catalytic triad Cys145 - His41 - Asp187, possibly disturbing the biological activity of SARS-CoV-2.

Identify potent SARS-CoV-2 main protease inhibitors via accelerated free energy perturbation-based virtual screening of existing drugs.

The COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become a global crisis. There is no therapeutic treatment specific for COVID-19. It is highly desirable to identify potential antiviral agents against SARS-CoV-2 from existing drugs available for other diseases and thus repurpose them for treatment of COVID-19. In general, a drug repurposing effort for treatment of a new disease, such as COVID-19, usually starts from a virtual screening of existing drugs, followed by experimental validation, but the actual hit rate is generally rather low with traditional computational methods. Here we report a virtual screening approach with accelerated free energy perturbation-based absolute binding free energy (FEP-ABFE) predictions and its use in identifying drugs targeting SARS-CoV-2 main protease (Mpro). The accurate FEP-ABFE predictions were based on the use of a restraint energy distribution (RED) function, making the practical FEP-ABFE-based virtual screening of the existing drug library possible. As a result, out of 25 drugs predicted, 15 were confirmed as potent inhibitors of SARS-CoV-2 Mpro The most potent one is dipyridamole (inhibitory constant Ki = 0.04 µM) which has shown promising therapeutic effects in subsequently conducted clinical studies for treatment of patients with COVID-19. Additionally, hydroxychloroquine (Ki = 0.36 µM) and chloroquine (Ki = 0.56 µM) were also found to potently inhibit SARS-CoV-2 Mpro We anticipate that the FEP-ABFE prediction-based virtual screening approach will be useful in many other drug repurposing or discovery efforts.

Novel Allosteric Effectors Targeting Human Transcription Factor TEAD.

The Hippo pathway is an evolutionary conserved signaling network involved in several cellular regulatory processes. Dephosphorylation and overexpression of Yes-associated proteins (YAPs) in the Hippo-off state are common in several types of solid tumors. YAP overexpression results in its nuclear translocation and interaction with transcriptional enhanced associate domain 1-4 (TEAD1-4) transcription factors. Covalent and non-covalent inhibitors have been developed to target several interaction sites between TEAD and YAP. The most targeted and effective site for these developed inhibitors is the palmitate-binding pocket in the TEAD1-4 proteins. Screening of a DNA-encoded library against the TEAD central pocket was performed experimentally to identify six new allosteric inhibitors. Inspired by the structure of the TED-347 inhibitor, chemical modification was performed on the original inhibitors by replacing secondary methyl amide with a chloromethyl ketone moiety. Various computational tools, including molecular dynamics, free energy perturbation, and Markov state model analysis, were employed to study the effect of ligand binding on the protein conformational space. Four of the six modified ligands were associated with enhanced allosteric communication between the TEAD4 and YAP1 domains indicated by the relative free energy perturbation to original molecules. Phe229, Thr332, Ile374, and Ile395 residues were revealed to be essential for the effective binding of the inhibitors.

Three-Dimensional-QSAR and Relative Binding Affinity Estimation of Focal Adhesion Kinase Inhibitors.

Precise binding affinity predictions are essential for structure-based drug discovery (SBDD). Focal adhesion kinase (FAK) is a member of the tyrosine kinase protein family and is overexpressed in a variety of human malignancies. Inhibition of FAK using small molecules is a promising therapeutic option for several types of cancer. Here, we conducted computational modeling of FAK-targeting inhibitors using three-dimensional structure-activity relationship (3D-QSAR), molecular dynamics (MD), and hybrid topology-based free energy perturbation (FEP) methods. The structure-activity relationship (SAR) studies between the physicochemical descriptors and inhibitory activities of the chemical compounds were performed with reasonable statistical accuracy using CoMFA and CoMSIA. These are two well-known 3D-QSAR methods based on the principle of supervised machine learning (ML). Essential information regarding residue-specific binding interactions was determined using MD and MM-PB/GBSA methods. Finally, physics-based relative binding free energy (ΔΔGRBFEA→B) terms of analogous ligands were estimated using alchemical FEP simulation. An acceptable agreement was observed between the experimental and computed relative binding free energies. Overall, the results suggested that using ML and physics-based hybrid approaches could be useful in synergy for the rational optimization of accessible lead compounds with similar scaffolds targeting the FAK receptor.

Free energy perturbation-based large-scale virtual screening for effective drug discovery against COVID-19.

As a theoretically rigorous and accurate method, FEP-ABFE (Free Energy Perturbation-Absolute Binding Free Energy) calculations showed great potential in drug discovery, but its practical application was difficult due to high computational cost. To rapidly discover antiviral drugs targeting SARS-CoV-2 Mpro and TMPRSS2, we performed FEP-ABFE-based virtual screening for ∼12,000 protein-ligand binding systems on a new generation of Tianhe supercomputer. A task management tool was specifically developed for automating the whole process involving more than 500,000 MD tasks. In further experimental validation, 50 out of 98 tested compounds showed significant inhibitory activity towards Mpro, and one representative inhibitor, dipyridamole, showed remarkable outcomes in subsequent clinical trials. This work not only demonstrates the potential of FEP-ABFE in drug discovery but also provides an excellent starting point for further development of anti-SARS-CoV-2 drugs. Besides, ∼500 TB of data generated in this work will also accelerate the further development of FEP-related methods.

Design of Drug Efficacy Guided by Free Energy Simulations of the ß2 -Adrenoceptor.

G-protein-coupled receptors (GPCRs) play important roles in physiological processes and are modulated by drugs that either activate or block signaling. Rational design of the pharmacological efficacy profiles of GPCR ligands could enable the development of more efficient drugs, but is challenging even if high-resolution receptor structures are available. We performed molecular dynamics simulations of the ß2 adrenergic receptor in active and inactive conformations to assess if binding free energy calculations can predict differences in ligand efficacy for closely related compounds. Previously identified ligands were successfully classified into groups with comparable efficacy profiles based on the calculated shift in ligand affinity upon activation. A series of ligands were then predicted and synthesized, leading to the discovery of partial agonists with nanomolar potencies and novel scaffolds. Our results demonstrate that free energy simulations enable design of ligand efficacy and the same approach can be applied to other GPCR drug targets.

Modeling receptor flexibility in the structure-based design of KRASG12C inhibitors.

KRAS has long been referred to as an 'undruggable' target due to its high affinity for its cognate ligands (GDP and GTP) and its lack of readily exploited allosteric binding pockets. Recent progress in the development of covalent inhibitors of KRASG12C has revealed that occupancy of an allosteric binding site located between the α3-helix and switch-II loop of KRASG12C-sometimes referred to as the 'switch-II pocket'-holds great potential in the design of direct inhibitors of KRASG12C. In studying diverse switch-II pocket binders during the development of sotorasib (AMG 510), the first FDA-approved inhibitor of KRASG12C, we found the dramatic conformational flexibility of the switch-II pocket posing significant challenges toward the structure-based design of inhibitors. Here, we present our computational approaches for dealing with receptor flexibility in the prediction of ligand binding pose and binding affinity. For binding pose prediction, we modified the covalent docking program CovDock to allow for protein conformational mobility. This new docking approach, termed as FlexCovDock, improves success rates from 55 to 89% for binding pose prediction on a dataset of 10 cross-docking cases and has been prospectively validated across diverse ligand chemotypes. For binding affinity prediction, we found standard free energy perturbation (FEP) methods could not adequately handle the significant conformational change of the switch-II loop. We developed a new computational strategy to accelerate conformational transitions through the use of targeted protein mutations. Using this methodology, the mean unsigned error (MUE) of binding affinity prediction were reduced from 1.44 to 0.89 kcal/mol on a set of 14 compounds. These approaches were of significant use in facilitating the structure-based design of KRASG12C inhibitors and are anticipated to be of further use in the design of covalent (and noncovalent) inhibitors of other conformationally labile protein targets.

Binding Studies and Lead Generation of Pteridin-7(8H)-one Derivatives Targeting FLT3.

Ligand modification by substituting chemical groups within the binding pocket is a popular strategy for kinase drug development. In this study, a series of pteridin-7(8H)-one derivatives targeting wild-type FMS-like tyrosine kinase-3 (FLT3) and its D835Y mutant (FL3D835Y) were studied using a combination of molecular modeling techniques, such as docking, molecular dynamics (MD), binding energy calculation, and three-dimensional quantitative structure-activity relationship (3D-QSAR) studies. We determined the protein-ligand binding affinity by employing molecular mechanics Poisson-Boltzmann/generalized Born surface area (MM-PB/GBSA), fast pulling ligand (FPL) simulation, linear interaction energy (LIE), umbrella sampling (US), and free energy perturbation (FEP) scoring functions. The structure-activity relationship (SAR) study was conducted using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA), and the results were emphasized as a SAR scheme. In both the CoMFA and CoMSIA models, satisfactory correlation statistics were obtained between the observed and predicted inhibitory activity. The MD and SAR models were co-utilized to design several new compounds, and their inhibitory activities were anticipated using the CoMSIA model. The designed compounds with higher predicted pIC50 values than the most active compound were carried out for binding free energy evaluation to wild-type and mutant receptors using MM-PB/GBSA, LIE, and FEP methods.

Oxidation shuts down an auto-inhibitory mechanism of von Willebrand factor.

The blood protein von Willebrand factor (VWF) is a key link between inflammation and pathological thrombus formation. In particular, oxidation of methionine residues in specific domains of VWF due to the release of oxidants in inflammatory conditions has been linked to an increased platelet-binding activity. However, the atomistic details of how methionine oxidation activates VWF have not been elucidated to date. Yet understanding the activation mechanism of VWF under oxidizing conditions can lead to the development of novel therapeutics that target VWF selectively under inflammatory conditions in order to reduce its thrombotic activity while maintaining its haemostatic function. In this manuscript, we used a combination of a dynamic flow assay and molecular dynamics (MD) simulations to investigate how methionine oxidation removes an auto-inhibitory mechanism of VWF. Results from the dynamic flow assay revealed that oxidation does not directly activate the A1 domain, which is the domain in VWF that contains the binding site to the platelet surface receptor glycoprotein Ibα (GpIbα), but rather removes the inhibitory function of the neighboring A2 and A3 domains. Furthermore, the MD simulations combined with free energy perturbation calculations suggested that methionine oxidation may destabilize the binding interface between the A1 and A2 domains leading to unmasking of the GpIbα-binding site in the A1 domain.

Free energy perturbation in the design of EED ligands as inhibitors of polycomb repressive complex 2 (PRC2) methyltransferase.

Free Energy Perturbation (FEP) calculations can provide high-confidence predictions of the interaction strength between a ligand and its protein target. We sought to explore a series of triazolopyrimidines which bind to the EED subunit of the PRC2 complex as potential anticancer therapeutics, using FEP calculations to inform compound design. Combining FEP predictions with a late-stage functionalisation (LSF) inspired synthetic approach allowed us to rapidly evaluate structural modifications in a previously unexplored region of the EED binding site. This approach generated a series of novel triazolopyrimidine EED ligands with improved physicochemical properties and which inhibit PRC2 methyltransferase activity in a cancer-relevant G401 cell line.

Precise force-field-based calculations of octanol-water partition coefficients for the SAMPL7 molecules.

We predicted water-octanol partition coefficients for the molecules in the SAMPL7 challenge with explicit solvent classical molecular dynamics (MD) simulations. Water hydration free energies and octanol solvation free energies were calculated with a windowed alchemical free energy approach. Three commonly used force fields (AMBER GAFF, CHARMM CGenFF, OPLS-AA) were tested. Special emphasis was placed on converging all simulations, using a criterion developed for the SAMPL6 challenge. In aggregate, over 1000 [Formula: see text]s of simulations were performed, with some free energy windows remaining not fully converged even after 1 [Formula: see text]s of simulation time. Nevertheless, the amount of sampling produced [Formula: see text] estimates with a precision of 0.1 log units or better for converged simulations. Despite being probably as fully sampled as can expected and is feasible, the agreement with experiment remained modest for all force fields, with no force field performing better than 1.6 in root mean squared error. Overall, our results indicate that a large amount of sampling is necessary to produce precise [Formula: see text] predictions for the SAMPL7 compounds and that high precision does not necessarily lead to high accuracy. Thus, fundamental problems remain to be solved for physics-based [Formula: see text] predictions.