Free Energy Perturbation Simulations Research Articles

A Novel Rational PROTACs Design and Validation via AI-Driven Drug Design Approach.

The rational design of novel drug candidates presents a formidable challenge in modern drug discovery. Proteolysis-targeting chimeras (PROTACs) drug design is particularly demanding due to their limited crystal structure availability and design of a viable small molecule to bridge the protein of interest (POI) and ubiquitin-protein ligase (E3). An integrated approach that combines superimposition techniques and deep neural networks is demonstrated in this study to leverage the power of deep learning and structural biology to generate structurally diverse molecules with enhanced binding affinities. The superimposition technique ensures the congruence of initial and new protein-ligand pairs, which are evaluated via subsequent comprehensive screening using the root-mean-square deviation (RMSD), binding free energy (BFE), and buried solvent-accessible surface area (SASA). The final candidates are subjected to the incorporation of molecular dynamics (MD) and free energy perturbation (FEP) simulations to provide a quantitative evaluation of relative binding energies, reinforcing the efficacy and reliability of the generated molecules. The outcomes of the generated novel PROTACs molecules exhibit comparable structural attributes while demonstrating superior binding affinities within the binding pockets when contrasted with those of the established cocrystal ternary complexes. To enhance the generalizability of the workflow, we chose the ternary structure of the cellular inhibitor of apoptosis protein 1 (cIAP1) and Bruton's Tyrosine Kinase (BTK) for validating the chemical properties generated from the processes. The new linker molecules additionally showed superior affinity from the simulations. In summary, this methodology serves as an effective workflow to align computational predictions with current limitations, thereby introducing a novel paradigm in AI-driven drug design.

Evolutionary sequence and structural basis for the distinct conformational landscapes of Tyr and Ser/Thr kinases

Protein kinases are molecular machines with rich sequence variation that distinguishes the two main evolutionary branches – tyrosine kinases (TKs) from serine/threonine kinases (STKs). Using a sequence co-variation Potts statistical energy model we previously concluded that TK catalytic domains are more likely than STKs to adopt an inactive conformation with the activation loop in an autoinhibitory folded conformation, due to intrinsic sequence effects. Here we investigate the structural basis for this phenomenon by integrating the sequence-based model with structure-based molecular dynamics (MD) to determine the effects of mutations on the free energy difference between active and inactive conformations, using a thermodynamic cycle involving many (n = 108) protein-mutation free energy perturbation (FEP) simulations in the active and inactive conformations. The sequence and structure-based results are consistent and support the hypothesis that the inactive conformation DFG-out Activation Loop Folded, is a functional regulatory state that has been stabilized in TKs relative to STKs over the course of their evolution via the accumulation of residue substitutions in the activation loop and catalytic loop that facilitate distinct substrate binding modes in trans and additional modes of regulation in cis for TKs.

Computational Screening of Hydrophobic Zeolites for the Removal of Emerging Organic Contaminants from Water.

The pollution of water resources by pharmaceuticals and agents of personal care products (PPCPs) poses an increasingly pressing issue that has received considerable attention from scientists and government agencies alike. Hydrophobic zeolites can serve as selective adsorbents to remove these contaminants from aqueous solution. So far, the adsorption of PPCPs in zeolites has often been investigated in case studies focusing on a small number of contaminants and one or a few zeolites. We present a computational screening approach to investigate the interaction of 53 PPCPs with 14 all-silica zeolites, aiming at a more comprehensive understanding of factors that are beneficial for a strong host-guest interaction and thus an efficient adsorption. The systems are modelled on the classical force field level of theory, allowing for the efficient computational treatment of a large number of PPCP-zeolite combinations and evaluated in terms of the interaction energy between PPCP and zeolite framework. For selected PPCP-zeolite combinations additional Free Energy Perturbation simulations are employed to compute Free Energies of Transfer between the aqueous phase and the adsorbed state. These results can serve as a starting point for experimental studies of relevant PPCP-zeolite combination or more in-depth theoretical investigations.

Abstract GS03-10: Allosteric PI3K-alpha inhibition overcomes on-target resistance to orthosteric inhibitors mediated by secondary PIK3CA mutations

Abstract Background: PIK3CA mutations occur in ~40% of HR-positive breast cancers, where alpelisib, an orthosteric PI3Kα inhibitor, is FDA-approved in combination with fulvestrant. Although prior studies have identified potential resistance mechanisms, such as PTEN loss, clinical acquired resistance to orthosteric PI3Kα inhibitors and the role of next-generation allosteric PI3Kα inhibitors remain poorly understood. Methods: To identify on-target and off-target alterations potentially mediating resistance to PI3Kα inhibitors, we used a targeted next-generation sequencing assay (Guardant360; Guardant Health) to analyze ctDNA in serially collected plasma samples from 32 patients with PIK3CA-mutated advanced HR-positive, HER2-negative breast cancer treated with alpelisib and inavolisib. In addition, we performed whole exome sequencing (WES) of 100 tissue samples collected from 8 autopsy series from patients with metastatic, PIK3CA-mutant HR-positive, HER2-negative breast cancer previously treated with PI3Kα inhibitors. Acquired alterations were prioritized through a combination of structural modeling and free-energy perturbation simulation and validated in genomically engineered PIK3CA mutant breast cancer cell lines T47D (PIK3CA H1047R-mutant) or MCF7 (PIK3CA E545K-mutant). Results: We observed that 50% of patients acquire genomic alterations within the PI3K-pathway, including PTEN loss and activating AKT1 mutations. Notably, while secondary PIK3CA mutations were previously reported to increase sensitivity to PI3Ka-inhibitors, we identified emergent secondary resistance mutations in PIK3CA that alter the inhibitor binding pocket including PIK3CA Q859K and PIK3CA W780R. Some mutations had differential effects on PI3Ka-selective vs. pan-PI3K inhibitors, but resistance induced by all mutations could be overcome by the novel allosteric pan-mutant-selective PI3Ka-inhibitor RLY-2608. Conclusion: In one of the largest patient cohorts analyzed to date, this study defines the clinical landscape of acquired resistance to PI3Ka inhibitors. Genomic alterations within the PI3K pathway represent a major mode of resistance and identify a novel class of secondary PIK3CA resistance mutations that can be overcome by an allosteric PI3Ka inhibitor. Together, these findings provide insights to guide strategies to overcome resistance in PIK3CA-mutated cancers. Citation Format: Andreas Varkaris, Ferran Fece de la Cruz, Elizabeth Martin, Bryanna Nordern, Nicholas Chevalier, Ignaty Leshchiner, Anastasia Stavridi, Janice Kim, Alkistis Papatheodoridi, Hakan Gunaydin, Brian Danysh, Laxmi Parida, Ioannis Sanidas, Yongli Ji, Kayao Lau, Gerburg Wulf, Aditya Bardia, Laura Spring, Steven Isakoff, Jochen Lennerz, Levi Pierce, Ermira Pazolli, Gad Getz, Ryan Corcoran, Dejan Juric. Allosteric PI3K-alpha inhibition overcomes on-target resistance to orthosteric inhibitors mediated by secondary PIK3CA mutations [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr GS03-10.

Reweighting from Molecular Mechanics Force Fields to the ANI-2x Neural Network Potential.

To achieve chemical accuracy in free energy calculations, it is necessary to accurately describe the system's potential energy surface and efficiently sample configurations from its Boltzmann distribution. While neural network potentials (NNPs) have shown significantly higher accuracy than classical molecular mechanics (MM) force fields, they have a limited range of applicability and are considerably slower than MM potentials, often by orders of magnitude. To address this challenge, Rufa et al. [Rufa et al. bioRxiv 2020, 10.1101/2020.07.29.227959.] suggested a two-stage approach that uses a fast and established MM alchemical energy protocol, followed by reweighting the results using NNPs, known as endstate correction or indirect free energy calculation. This study systematically investigates the accuracy and robustness of reweighting from an MM reference to a neural network target potential (ANI-2x) for an established data set in vacuum, using single-step free-energy perturbation (FEP) and nonequilibrium (NEQ) switching simulation. We assess the influence of longer switching lengths and the impact of slow degrees of freedom on outliers in the work distribution and compare the results to those of multistate equilibrium free energy simulations. Our results demonstrate that free energy calculations between NNPs and MM potentials should be preferably performed using NEQ switching simulations to obtain accurate free energy estimates. NEQ switching simulations between the MM potentials and NNPs are efficient, robust, and trivial to implement.

Potts Hamiltonian Models and Molecular Dynamics Free Energy Simulations for Predicting the Impact of Mutations on Protein Kinase Stability.

Single-point mutations in kinase proteins can affect their stability and fitness, and computational analysis of these effects can provide insights into the relationships among protein sequence, structure, and function for this enzyme family. To assess the impact of mutations on protein stability, we used a sequence-based Potts Hamiltonian model trained on a kinase family multiple-sequence alignment (MSA) to calculate the statistical energy (fitness) effects of mutations and compared these against relative folding free energies (ΔΔGs) calculated from all-atom molecular dynamics free energy perturbation (FEP) simulations in explicit solvent. The fitness effects of mutations in the Potts model (ΔEs) showed good agreement with experimental thermostability data (Pearson r = 0.68), similar to the correlation we observed with ΔΔGs predicted from structure-based relative FEP simulations. Recognizing the possible advantages of using Potts models to rapidly estimate protein stability effects of kinase mutations seen in cancer genomics data, we used the Potts statistical energy model to estimate the stability effects of 65 conservative and nonconservative mutations across three distinct kinases (Wee1, Abl1, and Cdc7) with somatic mutations reported in the Genomic Data Commons (GDC) database. The ΔEs of these mutations calculated from the Potts model are consistent with the corresponding ΔΔGs from FEP simulations (Pearson ratio of 0.72). The agreement between these methods suggests that the Potts model may be used as a sequence-based tool for high-throughput screening of mutational effects as part of a computational pipeline for predicting the stability effects of mutations. We also demonstrate how the scalability of the fitness-based Potts model calculations permits analyses that are not easily accessed using FEP simulations. To this end, we employed site-saturation mutagenesis in the Potts model in order to investigate the relative stability effects of mutations seen in different cancer evolutionary scenarios. We used this approach to analyze the effects of drug pressure in Abl kinase by contrasting the relative fitness penalties of somatic mutations seen in miscellaneous cancer types with those calculated for mutations associated with cancer drug resistance. We observed that, in contrast to somatic mutations of Abl seen in various tumors that appear to have evolved neutrally, cancer mutations that evolved under drug pressure in Abl-targeted therapies tend to preserve enzyme stability.

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.

Molecular mechanisms that cause cell death in LHON

Mitochondrial impairments, caused by mtDNA mutations, lead to RGC death and optic atrophy causing blindness in LHON. It was once thought that this was a disease of impaired bioenergetics. More recent thinking, considering the tempo of damage, studies in cybrids and a faithful mouse genetic model of LHON, has led to the realization that the problem is more that of the pathological generation of Reactive Oxygen Species (ROS). For example, synaptosome analysis from the brains of LHON mice show normal levels of ATP, but the blockage of Complex I activity was associated with very high levels of ROS. We hypothesized that the mtDNA mutation, specifically for 3460 in ND1, changed the atomic structures around COQ10 such that ROS were generated. In order to test that hypothesis, we conducted a series of experiments with statistical ensembles, built with Monte Carlo and Boltzmann sampling for simulations in computational chemistry. We began with the single amino acid mutation in ND1 of Complex I that switches hydrophobic alanine to hydrophilic threonine, at the entry of the Coenzyme Q10 binding channel. We explicated the mechanistic consequences of this mutation, using the tools of Molecular Dynamics and Free Energy Perturbation simulations. We showed that the mutant remained capable of binding Coenzyme Q10. Indeed, the mutated channel was even more proficient than the wild type in reducing ubiquinone to ubiquinol due to the closer average proximity of the Coenzyme Q10 headgroup to the chain of iron–sulfur clusters, from which it received electrons via quantum mechanical tunnelling. However, the mutation kinetically hindered the exit of the reduced (ubiquinol) form of Q10 from the ND1 channel, slowing the passage of its headgroup due to unfavourable electrostatics. This effect lead to overproduction of ROS via re‐oxidation of Coenzyme Q10 as electrons were sent back up the Iron–sulfur cluster chain, where the ROS production occurs. Another line of studies has shown that ROS lead to the death of RGCs affecting the smallest axons first. In those areas of the optic nerve in which larger axons are preponderate, the ROS are absorbed by the larger axons and little more happens. However, there is a dearth of large axons inferior temporally where the papillomacular bundle enters the optic nerve. Then, the ROS begins a reiterative and vicious cycle of cell death and the production of more ROS. This explains both the very peculiar spatial and temporal features of the optic neuropathy in LHON.

Alchemical approach performance in calculating the ligand-binding free energy.

Alchemical binding free energy calculations are one of the most accurate methods for estimating ligand-binding affinity. Assessing the accuracy of the approach over protein targets is one of the most interesting issues. The free energy difference of binding between a protein and a ligand was calculated via the alchemical approach. The alchemical approach exhibits satisfactory accuracy over four targets, including AmpC beta-lactamase (AmpC); glutamate receptor, ionotropic kainate 1 (GluK1); heat shock protein 90 (Hsp90); and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease (Mpro). In particular, the correlation coefficients between calculated binding free energies and the respective experiments over four targets range from 0.56 to 0.86. The affinity computed via free energy perturbation (FEP) simulations is overestimated over the experimental value. Particularly, the electrostatic interaction free energy rules the binding process of ligands to AmpC and GluK1. However, the van der Waals (vdW) interaction free energy plays an important role in the ligand-binding processes of HSP90 and SARS-CoV-2 Mpro. The obtained results associate with the hydrophilic or hydrophobic properties of the ligands. This observation may enhance computer-aided drug design.

Conformational Selectivity of ITK Inhibitors: Insights from Molecular Dynamics Simulations.

Interleukin-2-inducible T-cell kinase (ITK) regulates the response to T-cell receptor signaling and is a drug target for inflammatory and immunological diseases. Molecules that bind preferentially to the active form of ITK have low selectivity between kinases, whereas those that bind preferentially to the inactive form have high selectivity for ITK. Therefore, computational methods to predict the conformational selectivity of compounds are required to design highly selective ITK inhibitors. In this study, we performed absolute binding free-energy perturbation (ABFEP) simulations for 11 compounds on both active and inactive forms of ITK, and the calculated binding free energies were compared with experimental data. The conformational selectivity of 10 of the 11 compounds was correctly predicted using ABFEP. To investigate the mechanism underlying the stabilization of the active and inactive structures by the compounds, we performed extensive, conventional molecular dynamics simulations, which revealed that the compound-induced stabilization of the P-loop and linkage of conformational changes in L489, V419, F501, and M410 upon compound binding were critical factors. A guideline for designing inactive-form binders is proposed based on these key structural factors. The ABFEP and the created guidelines are expected to facilitate the discovery of highly selective ITK inhibitors.

Basic Residues at Position 11 of α-Conotoxin LvIA Influence Subtype Selectivity between α3β2 and α3β4 Nicotinic Receptors via an Electrostatic Mechanism.

Understanding the determinants of α-conotoxin (α-CTX) selectivity for different nicotinic acetylcholine receptor (nAChR) subtypes is a prerequisite for the design of tool compounds to study nAChRs. However, selectivity optimization of these small, disulfide-rich peptides is difficult not only because of an absence of α-CTX/nAChR co-structures but also because it is challenging to predict how a mutation to an α-CTX will alter its potency and selectivity. As a prototypical system to investigate selectivity, we employed the α-CTX LvIA that is 25-fold selective for the α3β2 nAChR over the related α3β4 nAChR subtype, which is a target for nicotine addiction. Using two-electrode voltage clamp electrophysiology, we identified LvIA[D11R] that is 2-fold selective for the α3β4 nAChR, reversing the subtype preference. This effect is specifically due to the change in charge and not shape of LvIA[D11R], as substitution of D11 with citrulline retains selectivity for the α3β2 nAChR. Furthermore, LvIA[D11K] shows a stronger reversal, with 4-fold selectivity for the α3β4 nAChR. Motivated by these findings, using site-directed mutagenesis, we found that β2[K79A] (I79 on β4), but not β2[K78A] (N78 on β4), largely restores the potency of basic mutants at position 11. Finally, to understand the structural basis of this effect, we used AlphaFold2 to generate models of LvIA in complex with both nAChR subtypes. Both models confirm the plausibility of an electrostatic mechanism to explain the data and also reproduce a broad range of potency and selectivity structure-activity relationships for LvIA mutants, as measured using free energy perturbation simulations. Our work highlights how electrostatic interactions can drive α-CTX selectivity and may serve as a strategy for optimizing the selectivity of LvIA and other α-CTXs.

Prediction of The Binding Mode of NP‐G2‐044 Targeting Fascin and Its Drug Resistance Mutations

AbstractFascin is a major actin‐binding protein (ABP) in filopodia and is highly expressed in metastatic tumors. To inhibit its function, a potent inhibitor called NP‐G2‐044 is currently in phase II clinical trials. However, the binding mode of this inhibitor remains unclear. In this study, the binding mode of NP‐G2‐044 in fascin and predicted drug resistance mutations is investigated. The rough binding position and pose can be deduce based on information from its derivant NP‐G2‐029, whose crystal structure with fascin has been solved. Molecular docking, free energy perturbation (FEP) simulation and metadynamics simulation are performed to confirm the binding pose. Based on the molecular docking results, two binding modes of NP‐G2‐044, pose A and B, are obtained, with pose A being more favorable. For pose A, the planar indazole moiety has the same orientation as NP‐G2‐029 in the complex with fascin, while for pose B, it rotates ≈90°. FEP and metadynamics simulations indicate that both binding poses are possible. Relative binding free energies for mutation effects calculated by FEP predict E215A and R217A are drug resistance mutations. These findings are important for further drug development targeting fascin.

Coenzyme Q10 trapping in mitochondrial complex I underlies Leber’s hereditary optic neuropathy

How does a single amino acid mutation occurring in the blinding disease, Leber's hereditary optic neuropathy (LHON), impair electron shuttling in mitochondria? We investigated changes induced by the m.3460 G>A mutation in mitochondrial protein ND1 using the tools of Molecular Dynamics and Free Energy Perturbation simulations, with the goal of determining the mechanism by which this mutation affects mitochondrial function. A recent analysis suggested that the mutation's replacement of alanine A52 with a threonine perturbs the stability of a region where binding of the electron shuttling protein, Coenzyme Q10, occurs. We found two functionally opposing changes involving the role of Coenzyme Q10. The first showed that quantum electron transfer from the terminal Fe/S complex, N2, to the Coenzyme Q10 headgroup, docked in its binding pocket, is enhanced. However, this positive adjustment is overshadowed by our finding that the mobility of Coenzyme Q10 in its oxidized and reduced states, entering and exiting its binding pocket, is disrupted by the mutation in a manner that leads to conditions promoting the generation of reactive oxygen species. An increase in reactive oxygen species caused by the LHON mutation has been proposed to be responsible for this optic neuropathy.

Can Free Energy Perturbation Simulations Coupled with Replica-Exchange Molecular Dynamics Study Ligands with Distributed Binding Sites?

Free energy perturbation coupled with replica exchange with solute tempering (FEP/REST) offers a rigorous approach to compute relative free energy changes for ligands. To determine the applicability of FEP/REST for the ligands with distributed binding poses, we considered two alchemical transformations involving three putative inhibitors I0, I1, and I2 of the Venezuelan equine encephalitis virus nuclear localization signal sequence binding to the importin-α (impα) transporter protein. I0 → I1 and I0 → I2 transformations, respectively, increase or decrease the polarity of the parent molecule. Our objective was three-fold─(i) to verify FEP/REST technical performance and convergence, (ii) to estimate changes in binding free energy ΔΔG, and (iii) to determine the utility of FEP/REST simulations for conformational binding analysis. Our results are as follows. First, our FEP/REST implementation properly follows FEP/REST formalism and produces converged ΔΔG estimates. Due to ligand inherent unbinding, the better FEP/REST strategy lies in performing multiple independent trajectories rather than extending their length. Second, I0 → I1 and I0 → I2 transformations result in overall minor changes in inhibitor binding free energy, slightly strengthening the affinity of I1 and weakening that of I2. Electrostatic interactions dominate binding interactions, determining the enthalpic changes. The two transformations cause opposite entropic changes, which ultimately govern binding affinities. Importantly, we confirm the validity of FEP/REST free energy estimates by comparing them with our previous REST simulations, directly probing binding of three ligands to impα. Third, we established that FEP/REST simulations can sample binding ensembles of ligands. Thus, FEP/REST can be applied (i) to study the energetics of the ligand binding without defined poses and showing minor differences in affinities |ΔΔG| ≲ 0.5 kcal/mol and (ii) to collect ligand binding conformational ensembles.

Molecular Simulations of Conformational Transitions within the Insulin Receptor Kinase Reveal Consensus Features in a Multistep Activation Pathway.

Modulating the transitions between active and inactive conformations of protein kinases is the primary means of regulating their catalytic activity, achieved by phosphorylation of the activation loop (A-loop). To elucidate the mechanism of this conformational activation, we applied the string method to determine the conformational transition path of insulin receptor kinase between the active and inactive conformations and the corresponding free-energy profiles with and without A-loop phosphorylation. The conformational change was found to proceed in three sequential steps: first, the flipping of the DFG motif of the active site; second, rotation of the A-loop; finally, the inward movement of the αC helix. The main energetic bottleneck corresponds to the conformational change in the A-loop, while changes in the DFG motif and αC helix occur before and after A-loop conformational change, respectively. In accordance with this, two intermediate states are identified, the first state just after the DFG flipping and the second state after the A-loop rotation. These intermediates exhibit structural features characteristic of the corresponding inactive and active conformations of other protein kinases. To understand the impact of A-loop phosphorylation on kinase conformation, the free energies of A-loop phosphorylation were determined at several states along the conformational transition path using the free-energy perturbation simulations. The calculated free energies reveal that while the unphosphorylated kinase interconverts between the inactive and active conformations, A-loop phosphorylation restricts access to the inactive conformation, thereby increasing the active conformation population. Overall, this study suggests a consensus mechanism of conformational activation between different protein kinases.

Implementation of solvation free energy framework to predict the vapor-liquid equilibrium behaviors for the water–hydrazine and ethanol–water azeotropic systems

An azeotrope is a special vapor–liquid equilibrium in which the composition of two coexisting phases is equal. Due to its strong deviation from the Raoult’s law, it poses a great challenge to the simple distillation process and the understanding of fundamental molecular mechanisms. In this study, we calculated the coupling work of solute species based on the solvation free energy framework to predict the vapor–liquid equilibrium (VLE) behavior and azeotrope point of mixtures. Two prototypical binary mixtures: the negative water-hydrazine and positive ethanol–water azeotrope were investigated using molecular dynamic simulations and the OPLS-AA force field. All the simulations were performed with the TIP3P water model, because it has a good capability to represent enthalpy properties. The molecular models of hydrazine and ethanol have been re-parameterized to reproduce the density and solvation free energy properties of pure substance. Using free energy perturbation simulation, the coupling work of each substance was studied against liquid mixture composition. The coupling work is also translated to x-y or T-x-y phase diagrams for comparison with available experimental data. The tendency of two azeotropes is that the more volatile components experience the less pronounced coupling work changes in their local environments as the azeotropes form. The coupling work is also divided into van der Waals and Coulomb contributions to investigate the interactions between particles. Lithium Chloride was investigated as an entrainer for the separation of ethanol–water system. The coupling works of binary mixtures were calculated at different salt concentrations to study the salt-out effect for the violate species and salt-in effect for the less violate species. By adding LiCl, it was found that the azeotrope point was efficiently eliminated at a salinity 0.05 mol fraction at 1 bar. All the results show that the simulation methodology of coupling work are reliable for calculating the liquid–vapor equilibrium behavior. This method is also valuable for selecting and designing new extractants for distillation process.

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.

Exploring the Effect of Halogenation in a Series of Potent and Selective A2B Adenosine Receptor Antagonists.

The modulation of the A2B adenosine receptor is a promising strategy in cancer (immuno) therapy, with A2BAR antagonists emerging as immune checkpoint inhibitors. Herein, we report a systematic assessment of the impact of (di- and mono-)halogenation at positions 7 and/or 8 on both A2BAR affinity and pharmacokinetic properties of a collection of A2BAR antagonists and its study with structure-based free energy perturbation simulations. Monohalogenation at position 8 produced potent A2BAR ligands irrespective of the nature of the halogen. In contrast, halogenation at position 7 and dihalogenation produced a halogen-size-dependent decay in affinity. Eight novel A2BAR ligands exhibited remarkable affinity (Ki < 10 nM), exquisite subtype selectivity, and enantioselective recognition, with some eutomers eliciting sub-nanomolar affinity. The pharmacokinetic profile of representative derivatives showed enhanced solubility and microsomal stability. Finally, two compounds showed the capacity of reversing the antiproliferative effect of adenosine in activated primary human peripheral blood mononuclear cells.

Investigating the Dynamic Binding Behavior of PMX53 Cooperating with Allosteric Antagonist NDT9513727 to C5a Anaphylatoxin Chemotactic Receptor 1 through Gaussian Accelerated Molecular Dynamics and Free-Energy Perturbation Simulations.

C5a anaphylatoxin chemotactic receptor 1 (C5aR1) is an important target in anti-inflammatory therapeutics. The cyclic peptide antagonist PMX53 binds to the orthosteric site located in the extracellular vestibule of C5aR1, and the non-peptide antagonist NDT9513727 binds to the allosteric site formed by the middle region of TM3 (trans-membrane helix), TM4, and TM5. We catch a sight of the variational binding mode of PMX53 during the Gaussian accelerated molecular dynamic (GaMD) simulations. In the binary complex of C5aR1 and PMX53, the PMX53 takes a dynamic binding mechanism during the simulation. Namely, the side chain of Arg6 of PMX53 extends to TM6-TM7 (pose 1) or swings to TM5 (pose 2), forming a salt bridge with Glu199. Meanwhile, in the ternary complex of C5aR1 with PMX53 and NDT9513727, the side chain of Arg6 of PMX53 swings to TM5 (pose 2) from extending to TM6-TM7 (pose 1) at the beginning of the GaMD simulation. In subsequent simulation, PMX53 stabilizes in the pose 2 binding mode by forming a stable salt bridge with Glu199. The free-energy perturbation (FEP) calculations demonstrate that pose 1 (ΔGbinding = -10.94 kcal/mol) is more stable in the binary complex and pose 2 (ΔGbinding = -7.91 kcal/mol) is unstable because of highly dynamic TM5. NDT9513727 interacts directly with TM4 and TM5 and stabilizes the hydrophobic stack between the extracellular sides of the two helices. Therefore, pose 2 (ΔGbinding = -16.27 kcal/mol) is notably stable than pose 1 (ΔGbinding = -9.78 kcal/mol) in the ternary complex. The identification of a novel binding mode of PMX53 and the detailed structural information of PMX53 interacting with a receptor obtained by GaMD simulations will be helpful in designing potent antagonists of C5aR1.

Large-scale application of free energy perturbation calculations for antibody design

Alchemical free energy perturbation (FEP) is a rigorous and powerful technique to calculate the free energy difference between distinct chemical systems. Here we report our implementation of automated large-scale FEP calculations, using the Amber software package, to facilitate antibody design and evaluation. In combination with Hamiltonian replica exchange, our FEP simulations aim to predict the effect of mutations on both the binding affinity and the structural stability. Importantly, we incorporate multiple strategies to faithfully estimate the statistical uncertainties in the FEP results. As a case study, we apply our protocols to systematically evaluate variants of the m396 antibody for their conformational stability and their binding affinity to the spike proteins of SARS-CoV-1 and SARS-CoV-2. By properly adjusting relevant parameters, the particle collapse problems in the FEP simulations are avoided. Furthermore, large statistical errors in a small fraction of the FEP calculations are effectively reduced by extending the sampling, such that acceptable statistical uncertainties are achieved for the vast majority of the cases with a modest total computational cost. Finally, our predicted conformational stability for the m396 variants is qualitatively consistent with the experimentally measured melting temperatures. Our work thus demonstrates the applicability of FEP in computational antibody design.