Standard Binding Free Energy Research Articles

Druglike Molecules Binding to Large Membrane Proteins: Absolute Binding Free Energy Computation.

In this research, we employed the alchemical double-decoupling method alongside restraining potentials, coupled with the FEPMD method, to ascertain the standard binding free energy of a drug-like molecule termed BHQ and three analogous compounds engineered with progressive addition of bulky para-alkyl groups binding to SERCA (Ca2+-ATPase of skeletal muscle sarcoplasmic reticulum). Integral transmembrane proteins represent crucial drug targets in numerous therapeutic interventions, presenting computational challenges due to their considerable system sizes. Our approach integrated the generalized born potential method and the spherical solvent boundary potential method, allowing us to explicitly focus on the active binding site while treating the remainder of the system implicitly. We evaluated contributions to the standard binding free energy from distinct interaction potentials: electrostatic, repulsive, dispersive, and restraining potentials, computed separately. The resulting absolute binding free energy of BHQ (11.63 kcal/mol) closely aligns with experimental measurements (10.56 kcal/mol). Notably, an accurate estimation of the absolute binding free energy was achieved for the simplest analog, created with the addition of a single para-methyl group. However, the analog with two para-methyl groups exhibited the highest binding free energy, which disagreed with experimental results. Determining the binding free energy of the BHQ analog engineered with three para-methyl groups presented challenges in convergence and resulted in the lowest free energy among the three.

Abstract 1352: Computational design of a cyclic peptide that inhibits the CTLA4 in T cells and the growth of lung carcinoma in mice

Abstract Proteins involved in immune checkpoint pathways, such as CTLA4, PD-1, and PD-L1, have become important targets for cancer immunotherapy; however, the development of small molecule drugs targeting these pathways has proven difficult due to the nature of their protein-protein interfaces. Here, using a hierarchy of computational techniques, we designed a cyclic peptide that binds CTLA4 and follow this with experimental verification of binding and biological activity, using bio-layer interferometry, cell culture, and a lung cancer mouse model in immunocompetent mice. Beginning from a template excised from the x-ray structure of the CTLA4:B7-2 complex, we generate several peptide sequences using Rosetta, a protein modelling program. These peptides are cyclized head-to-tail to improve structural and proteolytic stability and screened using molecular dynamics simulation and MM-GBSA calculation. The standard binding free energies for shortlisted peptides are then calculated in explicit-solvent simulation using a rigorous multistep Binding Free Energy Estimator (BFEE). The most promising peptide, cyc-EIDTVLTPTGWVAKRYS (hereafter CTLA4-ip), yields the standard free energy -6.6 ± 3.5 kcal/mol, which corresponds to a dissociation constant of 15 μmol/L. The binding affinity of this peptide for CTLA4 is measured experimentally (31 ± 4 μmol/L) using bio-layer interferometry. Pharmacokinetics of CTLA4-ip in a cell culture with Lewis lung carcinoma (LLC) cells and in mice revealed that this peptide is significantly more stable in both cell culture and in mice as compared to a control non-cyclic scrambled-sequence. Treatment with CTLA4-ip inhibited cancer cell growth in a co-culture of LLC cells and LLC cell-antigen primed murine T cells. Intraperitoneal administration of the CTLA4-ip (10mg/kg/day, every other day totaling 4 doses) markedly inhibited lung tumor growth in mice with an orthotropic LLC allograft model. Efficacy of the tumor growth inhibition by the CTLA4-ip was similar to that by an anti-PD-L1 antibody (10mg/kg/day, every two days totaling 3 doses). These results strongly suggest that this novel CTLA4-ip works as an immune checkpoint inhibitor to CTLA4 and is usable for lung cancer treatment. This research was supported by Kansas State University Johnson Cancer Research center (MT), Midwest Biomedical Accelerator Consortium (MT, JC), National Cancer Institute MT, JC) and the National Science Foundation (JC). Citation Format: Ravindra Thakkar, Deepa Upreti, Susumu Ishiguro, Geraldine Magnin, Keshari Sudasinghe, Anabelle Hall, Sarah DeVader, Masaaki Tamura, Jeffrey Comer. Computational design of a cyclic peptide that inhibits the CTLA4 in T cells and the growth of lung carcinoma in mice [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1352.

Molecular simulations of doxorubicin complexed with native and modified cyclodextrins in water

In this study, all-atom molecular dynamics simulations have been performed to investigate from a nanoscopic point of view the interactions in supramolecular complexes formed between doxorubicin and cyclodextrins in an aqueous phase at a temperature of 310 K. All the simulations are on the time scales of microseconds and are carried out on High Performance Computing resources provided by the Greek Supercomputer ARIS. Doxorubicin, which is a well-known anticancer drug belonging to the class of anthracyclines, is simulated with two cyclodextrins of broad utilization in drug delivery, i.e.: γ-cyclodextrin and 2-hydroxypropyl-β-cyclodextrin. In both cases, the complexation process takes place spontaneously during the course of the simulations and a detailed analysis of the formed complexes is conducted in terms of structural, dynamical and thermodynamic properties. In addition, we present a quantitative analysis of how doxorubicin affects, upon complexation, the geometry, the hydrogen bonding network and the hydration of the selected cyclodextrins. Special attention is paid to the thermodynamic description of the simulated systems. More specifically, the standard binding free energy is calculated with an extended version of the linear interaction energy method. The presented in silico approach reveals that doxorubicin forms stable complexes with the chosen cyclodextrin molecules, there is a water solubility enhancement of the drug with respect to its unbound state and also cyclodextrins act protectively to the drug preventing undesired effects, such as the glycosidic degradation. Excellent agreement is found between the presented results and published in silico and experimental findings.

Contribution of Signaling Partner Association to Strigolactone Receptor Selectivity.

The parasitic plant witchweed, Striga hermonthica, results in agricultural losses of billions of dollars per year. It perceives its host via plant hormones called strigolactones, which act as germination stimulants for witchweed. Strigolactone signaling involves substrate binding to the strigolactone receptor, followed by substrate hydrolysis and a conformational change from an inactive, or open state, to an active, or closed state. In the active state, the receptor associates with a signaling partner, MAX2. Recently, it was shown that this MAX2 association process acts as a strong contributor to the uniquely high signaling activity observed in ShHTL7; however, it is unknown why ShHTL7 has enhanced MAX2 association affinity. Using an umbrella sampling molecular dynamics approach, we characterized the association processes of AtD14, ShHTL7, a mutant of ShHTL7, and ShHTL6 with MAX2 homologue OsD3. From these results, we show that ShHTL7 has an enhanced standard binding free energy of OsD3 compared to those of the other receptors. Additionally, our results suggest that the overall topology of the T2/T3 helix region is likely an important modulator of MAX2 binding. Thus, differences in MAX2 association, modulated by differences in the T2/T3 helix region, are a contributor to differences in signaling activity between different strigolactone receptors.

Novel chalcone derivatives of ursolic acid as acetylcholinesterase inhibitors: Synthesis, characterization, biological activity, ADME prediction, molecular docking and molecular dynamics studies

Acetylcholinesterase (AChE) is a critical target in the prevention of Alzheimer's Disease (AD) progression. With the goal of developing potential acetylcholinesterase inhibitors, a new series chalcone derivatives of ursolic acid (4–11) were synthesized and characterized by means of their NMR, FTIR and HRMS spectral data. hAChE inhibition abilities of all compounds were tested in vitro. Compounds 10, 8 and 7 showed high hAChE inhibitory potential with the IC50 values of 1.82, 4.65 and 5.36 µM respectively. When compared to the standard inhibitor (Galantamine, IC50= 1.96 µM), compound 10 was the most potent derivative. After performing kinetic studies, KM constant and Vmax value of hAChE were determined as 0.059 mM and 0.052 EU/ml respectively. Inhibition type of compound 10 was found as competitive and Ki constant was calculated as 1.31 ± 0.18 µM. The inhibition mechanism of compounds was predicted by molecular docking, MM/GBSA analysis, molecular dynamics simulation and ADME studies. Molecular docking studies showed that compounds 10, 8, and 7 have highest binding scores (-9.235, -8.827, -8.645 kcal/mol, respectively), while the standard inhibitor galantamine exhibited binding score of -5.106 kcal/mol. According to the MM/GBSA analysis, compounds 10 and 7 displayed binding free energies of -46.19 and -49.19 kcal/mol, respectively, which were higher than the standard's binding free energy of -41.82 kcal/mol. Molecular dynamic simulations were confirmed successful placement of the compounds 10, 8 and 7 in the active site of AChE. The in silico ADME predictions indicated that compound 10 has acceptable potency as a drug in treatment of AD.

Uncovering the Kinetic Characteristics and Degradation Preference of PROTAC Systems with Advanced Theoretical Analyses.

Proteolysis-targeting chimeras (PROTACs), which can selectively induce the degradation of target proteins, represent an attractive technology in drug discovery. A large number of PROTACs have been reported, but due to the complicated structural and kinetic characteristics of the target-PROTAC-E3 ligase ternary interaction process, the rational design of PROTACs is still quite challenging. Here, we characterized and analyzed the kinetic mechanism of MZ1, a PROTAC that targets the bromodomain (BD) of the bromodomain and extra terminal (BET) protein (Brd2, Brd3, or Brd4) and von Hippel-Lindau E3 ligase (VHL), from the kinetic and thermodynamic perspectives of view by using enhanced sampling simulations and free energy calculations. The simulations yielded satisfactory predictions on the relative residence time and standard binding free energy (rp > 0.9) for MZ1 in different BrdBD-MZ1-VHL ternary complexes. Interestingly, the simulation of the PROTAC ternary complex disintegration illustrates that MZ1 tends to remain on the surface of VHL with the BD proteins dissociating alone without a specific dissociation direction, indicating that the PROTAC prefers more to bind with E3 ligase at the first step in the formation of the target-PROTAC-E3 ligase ternary complex. Further exploration of the degradation difference of MZ1 in different Brd systems shows that the PROTAC with higher degradation efficiency tends to leave more lysine exposed on the target protein, which is guaranteed by the stability (binding affinity) and durability (residence time) of the target-PROTAC-E3 ligase ternary complex. It is quite possible that the underlying binding characteristics of the BrdBD-MZ1-VHL systems revealed by this study may be shared by different PROTAC systems as a general rule, which may accelerate rational PROTAC design with higher degradation efficiency.

Computational Evaluation of Peptide-Protein Binding Affinities: Application of Potential of Mean Force Calculations to SH2 Domains.

Many biological functions are mediated by protein-protein interactions (PPIs), often involving specific structural modules, such as SH2 domains. Inhibition of PPIs is a pharmaceutical strategy of growing importance. However, a major challenge in the design of PPI inhibitors is the large interface involved in these interactions, which, in many cases, makes inhibition by small organic molecules ineffective. Peptides, which cover a wide range of dimensions and can be opportunely designed to mimic protein sequences at PPI interfaces, represent a valuable alternative to small molecules. Computational techniques able to predict the binding affinity of peptides for the target domain or protein represent a crucial stage in the workflow for the design of peptide-based drugs. This chapter describes a protocol to obtain the potential of mean force (PMF) for peptide-SH2 domain binding, starting from umbrella sampling (US) molecular dynamics (MD) simulations. The PMF profiles can be effectively used to predict the relative standard binding free energies of different peptide sequences.

When the Dust Has Settled: Calculation of Binding Affinities from First Principles for SARS-CoV-2 Variants with Quantitative Accuracy.

Accurate determination of binding free energy is pivotal for the study of many biological processes and has been applied in a number of theoretical investigations to compare the affinity of severe acute respiratory syndrome coronavirus 2 variants toward the host cell. Diversity of these variants challenges the development of effective general therapies, their transmissibility relying either on an increased affinity toward their dedicated human receptor, the angiotensin-converting enzyme 2 (ACE2), or on escaping the immune response. Now that robust structural data are available, we have determined with utmost accuracy the standard binding free energy of the receptor-binding domain to the most widespread variants, namely, Alpha, Beta, Delta, and Omicron BA.2, as well as the wild type (WT) in complex either with ACE2 or with antibodies, namely, S2E12 and H11-D4, using a rigorous theoretical framework that combines molecular dynamics and potential-of-mean-force calculations. Our results show that an appropriate starting structure is crucial to ensure appropriate reproduction of the binding affinity, allowing the variants to be compared. They also emphasize the necessity to apply the relevant methodology, bereft of any shortcut, to account for all the contributions to the standard binding free energy. Our estimates of the binding affinities support the view that while the Alpha and Beta variants lean on an increased affinity toward the host cell, the Delta and Omicron BA.2 variants choose immune escape. Moreover, the S2E12 antibody, already known to be active against the WT (Starr et al., 2021; Mlcochova et al., 2021), proved to be equally effective against the Delta variant. In stark contrast, H11-D4 retains a low affinity toward the WT compared to that of ACE2 for the latter. Assuming robust structural information, the methodology employed herein successfully addresses the challenging protein–protein binding problem in the context of coronavirus disease 2019 while offering promising perspectives for predictive studies of ever-emerging variants.

A Curvilinear-Path Umbrella Sampling Approach to Characterizing the Interactions Between Rapamycin and Three FKBP12 Variants.

Rapamycin is an immunosuppressant macrolide that exhibits anti-proliferative properties through inhibiting the mTOR kinase. In fact, the drug first associates with the FKBP12 enzyme before interacting with the FRB domain of its target. Despite the availability of structural and thermodynamic information on the interaction of FKBP12 with rapamycin, the energetic and mechanistic understanding of this process is still incomplete. We recently reported a multiple-walker umbrella sampling simulation approach to characterizing the protein–protein interaction energetics along curvilinear paths. In the present paper, we extend our investigations to a protein-small molecule duo, the FKBP12•rapamycin complex. We estimate the binding free energies of rapamycin with wild-type FKBP12 and two mutants in which a hydrogen bond has been removed, D37V and Y82F. Furthermore, the underlying mechanistic details are analyzed. The calculated standard free energies of binding agree well with the experimental data, and the roles of the hydrogen bonds are shown to be quite different for each of these two mutated residues. On one hand, removing the carboxylate group of D37 strongly destabilizes the association; on the other hand, the hydroxyl group of Y82 is nearly unnecessary for the stability of the complex because some nonconventional, cryptic, indirect interaction mechanisms seem to be at work.

Hazardous Shortcuts in Standard Binding Free Energy Calculations.

Calculating the standard binding free energies of protein-protein and protein-ligand complexes from atomistic molecular dynamics simulations in explicit solvent is a problem of central importance in computational biophysics. A rigorous strategy for carrying out such calculations is the so-called "geometrical route". In this method, two molecular objects are progressively separated from one another in the presence of orientational and conformational restraints serving to control the change in configurational entropy that accompanies the dissociation process, thereby allowing the computations to converge within simulations of affordable length. Although the geometrical route provides a rigorous theoretical framework, a tantalizing computational shortcut consists of simply leaving out such orientational and conformational degrees of freedom during the separation process. Here the accuracy and convergence of the two approaches are critically compared in the case of two protein-ligand complexes (Abl kinase-SH3:p41 and MDM2-p53:NVP-CGM097) and three protein-protein complexes (pig insulin dimer, SARS-CoV-2 spike RBD:ACE2, and CheA kinase-P2:CheY). The results of the simulations that strictly follow the geometrical route match the experimental standard binding free energies within chemical accuracy. In contrast, simulations bereft of geometrical restraints converge more poorly, yielding inconsistent results that are at variance with the experimental measurements. Furthermore, the orientational and positional time correlation functions of the protein in the unrestrained simulations decay over several microseconds, a time scale that is far longer than the typical simulation times of the geometrical route, which explains why those simulations fail to sample the relevant degrees of freedom during the separation process of the complexes.

Molecular Dynamics Simulation Investigation of the Binding and Interaction of the EphA6-Odin Protein Complex.

Protein-protein interaction plays an important role in the development of almost all cells. Elucidating the dynamic binding and affinity of a protein-protein complex is essential for understanding the biological functions of proteins. EphA6 and Odin proteins are members of the Eph (erythropoietin-producing hepatocyte) receptor family and the Anks (ankyrin repeat and sterile α motif domain-containing) family, respectively. Odin significantly functions in regulating endocytosis, degradation, and stability of EphA receptors. In this work, the key residues of the interaction interface were determined through a hydrogen bond, root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), and dynamic correlation analysis of the conventional molecular dynamics (MD) simulations. The calculated standard binding free energy, -7.92 kcal/mol, between EphA6 and Odin is quite consistent with the experimental measurement value, -8.73 kcal/mol. By the combination of several MD simulation techniques, our investigation of the binding process reveals the detailed representative characteristics of the entire binding pathway at the molecular level. Based on the obtained potential of the mean force (PMF) curve, the analysis of the simulation trajectories shows that the residue Arg1013 in the receptor EphA6 is responsible for capturing Asp739 and Asp740 in the ligand Odin during the initial stage of binding. In the later stage of binding, the hydrogen bonds and salt bridges between a series of residues Lys973, Leu1007, Gly1009, His1010, and Arg1012 in the receptor and residues Leu735, Asn736, Asp739, Asp740, and Asp753 in the ligand mainly contribute to the stability of the protein complex. In addition, the specific change process of the receptor-ligand-binding mode is also clarified during the binding process. Our present simulation will promote a deep understanding of the protein-protein interaction, and the identified key interresidue interaction will be theoretical guidance for the design of protein drugs.

Standard Binding Free Energy and Membrane Desorption Mechanism for a Phospholipase C.

Peripheral membrane proteins (PMPs) bind temporarily to cellular membranes and play important roles in signaling, lipid metabolism, and membrane trafficking. Obtaining accurate membrane-PMP affinities using experimental techniques is more challenging than for protein-ligand affinities in an aqueous solution. At the theoretical level, calculation of the standard protein-membrane binding free energy using molecular dynamics simulations remains a daunting challenge owing to the size of the biological objects at play, the slow lipid diffusion, and the large variation in configurational entropy that accompanies the binding process. To overcome these challenges, we used a computational framework relying on a series of potential-of-mean-force (PMF) calculations including a set of geometrical restraints on collective variables. This methodology allowed us to determine the standard binding free energy of a PMP to a phospholipid bilayer using an all-atom force field. Bacillus thuringiensis phosphatidylinositol-specific phospholipase C (BtPI-PLC) was chosen due to its importance as a virulence factor and owing to the host of experimental affinity data available. We computed a standard binding free energy of -8.2 ± 1.4 kcal/mol in reasonable agreement with the reported experimental values (-6.6 ± 0.2 kcal/mol). In light of the 2.3-μs separation PMF calculation, we investigated the mechanism whereby BtPI-PLC disengages from interactions with the lipid bilayer during separation. We describe how a short amphipathic helix engages in transitory interactions to ease the passage of its hydrophobes through the interfacial region upon desorption from the bilayer.

Accurate determination of protein:ligand standard binding free energies from molecular dynamics simulations.

Designing a reliable computational methodology to calculate protein:ligand standard binding free energies is extremely challenging. The large change in configurational enthalpy and entropy that accompanies the association of ligand and protein is notoriously difficult to capture in naive brute-force simulations. Addressing this issue, the present protocol rests upon a rigorous statistical mechanical framework for the determination of protein:ligand binding affinities together with the comprehensive Binding Free-Energy Estimator 2 (BFEE2) application software. With the knowledge of the bound state, available from experiments or docking, application of the BFEE2 protocol with a reliable force field supplies in a matter of days standard binding free energies within chemical accuracy, for a broad range of protein:ligand complexes. Limiting undesirable human intervention, BFEE2 assists the end user in preparing all the necessary input files and performing the post-treatment of the simulations towards the final estimate of the binding affinity.

Dissociation Pathways of the p53 DNA Binding Domain from DNA and Critical Roles of Key Residues Elucidated by dPaCS-MD/MSM.

p53 is a transcriptional factor that regulates cell response to a variety of stresses. About a half of all human tumors contain p53 mutations, and the accumulation of mutations in the DNA binding domain of p53 (p53-DBD) can cause destabilization of p53 and its complex with DNA. To identify the key residues of the p53-DBD/DNA binding and to understand the dissociation mechanisms of the p53-DBD/DNA complex, the dissociation process of p53-DBD from a DNA duplex that contains the consensus sequence (the specific target of p53-DBD) was investigated by a combination of dissociation parallel cascade selection molecular dynamics (dPaCS-MD) and the Markov state model (MSM). This combination (dPaCS-MD/MSM) enabled us to simulate dissociation of the two large molecules based on an all-atom model with a short simulation time (11.2 ± 2.2 ns per trial) and to analyze dissociation pathways, free energy landscape (FEL), and binding free energy. Among 75 trials of dPaCS-MD, p53-DBD dissociated first from the major groove and then detached from the minor groove in 93% of the cases, while 7% of the cases unbinding from the minor groove occurred first. Minor groove binding is mainly stabilized by R248, identified as the most important residue that tightly binds deep inside the minor groove. The standard binding free energy calculated from the FEL was -10.9 ± 0.4 kcal/mol, which agrees with an experimental value of -11.1 kcal/mol. These results indicate that the dPaCS-MD/MSM combination can be a powerful tool to investigate dissociation mechanisms of two large molecules. Analysis of the p53 key residues for DNA binding indicates high correlations with cancer-related mutations, confirming that impairment of the interactions between p53-DBD and DNA can be frequently related to cancer.

Machine Learning and Enhanced Sampling Simulations for Computing the Potential of Mean Force and Standard Binding Free Energy.

Computational capabilities are rapidly increasing, primarily because of the availability of GPU-based architectures. This creates unprecedented simulative possibilities for the systematic and robust computation of thermodynamic observables, including the free energy of a drug binding to a target. In contrast to calculations of relative binding free energy, which are nowadays widely exploited for drug discovery, we here push the boundary of computing the binding free energy and the potential of mean force. We introduce a novel protocol that leverages enhanced sampling, machine learning, and ad hoc algorithms to limit human intervention, computing time, and free parameters in free energy calculations. We first validate the method on a host–guest system, and then we apply the protocol to glycogen synthase kinase 3 beta, a protein kinase of pharmacological interest. Overall, we obtain a good correlation with experimental values in relative and absolute terms. While we focus on protein–ligand binding, the strategy is of broad applicability to any complex event that can be described with a path collective variable. We systematically discuss key details that influence the final result. The parameters and simulation settings are available at PLUMED-NEST to allow full reproducibility.

Accuracy of Alternate Nonpolarizable Force Fields for the Determination of Protein-Ligand Binding Affinities Dominated by Cation-π Interactions.

Modifying pair-specific Lennard-Jones parameters through the nonbonded FIX (NBFIX) feature of the CHARMM36 force field has proven cost-effective for improving the description of cation-π interactions in biological objects by means of pairwise additive potential energy functions. Here, two sets of newly optimized CHARMM36 force-field parameters including NBFIX corrections, coined CHARMM36m-NBF and CHARMM36-WYF, and the original force fields, namely CHARMM36m and Amber ff14SB, are used to determine the standard binding free energies of seven protein-ligand complexes containing cation-π interactions. Compared with precise experimental measurements, our results indicate that the uncorrected, original force fields significantly underestimate the binding free energies, with a mean error of 5.3 kcal/mol, while the mean errors of CHARMM36m-NBF and CHARMM36-WYF amount to 0.8 and 2.1 kcal/mol, respectively. The present study cogently demonstrates that the use of modified parameters jointly with NBFIX corrections dramatically increases the accuracy of the standard binding free energy of protein-ligand complexes dominated by cation-π interactions, most notably with CHARMM36m-NBF.

Alchemical Transfer Approach to Absolute Binding Free Energy Estimation.

The alchemical transfer method (ATM) for the calculation of standard binding free energies of noncovalent molecular complexes is presented. The method is based on a coordinate displacement perturbation of the ligand between the receptor binding site and the explicit solvent bulk and a thermodynamic cycle connected by a symmetric intermediate in which the ligand interacts with the receptor and solvent environments with equal strength. While the approach is alchemical, the implementation of the ATM is as straightforward as that for physical pathway methods of binding. The method is applicable, in principle, with any force field, as it does not require splitting the alchemical transformations into electrostatic and nonelectrostatic steps, and it does not require soft-core pair potentials. We have implemented the ATM as a freely available and open-source plugin of the OpenMM molecular dynamics library. The method and its implementation are validated on the SAMPL6 SAMPLing host-guest benchmark set. The work paves the way to streamlined alchemical relative and absolute binding free energy implementations on many molecular simulation packages and with arbitrary energy functions including polarizable, quantum-mechanical, and artificial neural network potentials.

DeepBAR: A Fast and Exact Method for Binding Free Energy Computation.

The fast and accurate calculation of standard binding free energy has many important applications. Existing methodologies struggle at balancing accuracy and efficiency. We introduce a new method to compute binding free energy using deep generative models and the Bennett acceptance ratio method (DeepBAR). Compared to the rigorous potential of mean force (PMF) approach that requires sampling from intermediate states, DeepBAR is an order-of-magnitude more efficient as demonstrated in a series of host-guest systems. Notably, DeepBAR is exact and does not suffer from approximations for entropic contributions used in methods such as the molecular mechanics energy combined with the generalized Born and surface area continuum solvation (MM/GBSA). We anticipate DeepBAR to be a valuable tool for computing standard binding free energy used in drug design.

Binding free energy of protein/ligand complexes calculated using dissociation Parallel Cascade Selection Molecular Dynamics and Markov state model.

We recently proposed a computational procedure to simulate the dissociation of protein/ligand complexes using the dissociation Parallel Cascade Selection Molecular Dynamics simulation (dPaCS-MD) method and to analyze the generated trajectories using the Markov state model (MSM). This procedure, called dPaCS-MD/MSM, enables calculation of the dissociation free energy profile and the standard binding free energy. To examine whether this method can reproduce experimentally determined binding free energies for a variety of systems, we used it to investigate the dissociation of three protein/ligand complexes: trypsin/benzamine, FKBP/FK506, and adenosine A2A receptor/T4E. First, dPaCS-MD generated multiple dissociation pathways within a reasonable computational time for all the complexes, although the complexes differed significantly in the size of the molecules and in intermolecular interactions. Subsequent MSM analyses produced free energy profiles for the dissociations, which provided insights into how each ligand dissociates from the protein. The standard binding free energies obtained by dPaCS-MD/MSM are in good agreement with experimental values for all the complexes. We conclude that dPaCS-MD/MSM can accurately calculate the binding free energies of these complexes.

SAMPL7 blind predictions using nonequilibrium alchemical approaches

In the context of the SAMPL7 challenge, we computed, employing a non-equilibrium (NE) alchemical technique, the standard binding free energy of two series of host-guest systems, involving as a host the Isaac's TrimerTrip, a Cucurbituril-like open cavitand, and the Gilson's Cyclodextrin derivatives. The adopted NE alchemy combines enhanced sampling molecular dynamics simulations with driven fast out-of-equilibrium alchemical trajectories to recover the free energy via the Jarzynski and Crooks NE theorems. The GAFF2 non-polarizable force field was used for the parametrization. Performances were acceptable and similar in accuracy to those we submitted for Gibb's Deep Cavity Cavitands in the previous SAMPL6 host-guest challenge, confirming the reliability of the computational approach and exposing, in some cases, some important deficiencies of the GAFF2 non-polarizable force field.