Structure and ligand-based drug discovery of IL-4 inhibitors via interaction-energy-based learning approaches

Binding free energy calculations that make use of alchemical pathways are becoming increasingly feasible thanks to advances in hardware and algorithms. Although relative binding free energy (RBFE) calculations are starting to find widespread use, absolute binding free energy (ABFE) calculations are still being explored mainly in academic settings due to the high computational requirements and still uncertain predictive value. However, in some drug design scenarios, RBFE calculations are not applicable and ABFE calculations could provide an alternative. Computationally cheaper end-point calculations in implicit solvent, such as molecular mechanics Poisson–Boltzmann surface area (MMPBSA) calculations, could too be used if one is primarily interested in a relative ranking of affinities. Here, we compare MMPBSA calculations to previously performed absolute alchemical free energy calculations in their ability to correlate with experimental binding free energies for three sets of bromodomain–inhibitor pairs. Different MMPBSA approaches have been considered, including a standard single-trajectory protocol, a protocol that includes a binding entropy estimate, and protocols that take into account the ligand hydration shell. Despite the improvements observed with the latter two MMPBSA approaches, ABFE calculations were found to be overall superior in obtaining correlation with experimental affinities for the test cases considered. A difference in weighted average Pearson () and Spearman () correlations of 0.25 and 0.31 was observed when using a standard single-trajectory MMPBSA setup ( = 0.64 and = 0.66 for ABFE; = 0.39 and = 0.35 for MMPBSA). The best performing MMPBSA protocols returned weighted average Pearson and Spearman correlations that were about 0.1 inferior to ABFE calculations: = 0.55 and = 0.56 when including an entropy estimate, and = 0.53 and = 0.55 when including explicit water molecules. Overall, the study suggests that ABFE calculations are indeed the more accurate approach, yet there is also value in MMPBSA calculations considering the lower compute requirements, and if agreement to experimental affinities in absolute terms is not of interest. Moreover, for the specific protein–ligand systems considered in this study, we find that including an explicit ligand hydration shell or a binding entropy estimate in the MMPBSA calculations resulted in significant performance improvements at a negligible computational cost.

Binding free energy calculations predict the potency of compounds to protein binding sites in a physically rigorous manner and see broad application in prioritizing the synthesis of novel drug candidates. Relative binding free energy (RBFE) calculations have emerged as an industry-standard approach to achieve highly accurate rank-order predictions of the potency of related compounds; however, this approach requires that the ligands share a common scaffold and a common binding mode, restricting the methods' domain of applicability. This is a critical limitation since complex modifications to the ligands, especially core hopping, are very common in drug design. Absolute binding free energy (ABFE) calculations are an alternate method that can be used for ligands that are not congeneric. However, ABFE suffers from a known problem of long convergence times due to the need to sample additional degrees of freedom within each system, such as sampling rearrangements necessary to open and close the binding site. Here, we report on an alternative method for RBFE, called Separated Topologies (SepTop), which overcomes the issues in both of the aforementioned methods by enabling large scaffold changes between ligands with a convergence time comparable to traditional RBFE. Instead of only mutating atoms that vary between two ligands, this approach performs two absolute free energy calculations at the same time in opposite directions, one for each ligand. Defining the two ligands independently allows the comparison of the binding of diverse ligands without the artificial constraints of identical poses or a suitable atom-atom mapping. This approach also avoids the need to sample the unbound state of the protein, making it more efficient than absolute binding free energy calculations. Here, we introduce an implementation of SepTop. We developed a general and efficient protocol for running SepTop, and we demonstrated the method on four diverse, pharmaceutically relevant systems. We report the performance of the method, as well as our practical insights into the strengths, weaknesses, and challenges of applying this method in an industrial drug design setting. We find that the accuracy of the approach is sufficiently high to rank order ligands with an accuracy comparable to traditional RBFE calculations while maintaining the additional flexibility of SepTop.

Absolute Binding Free Energy Calculations Using Molecular Dynamics Simulations with Restraining Potentials

The accurate prediction of absolute protein-ligand binding free energies is one of the grand challenge problems of computational science. Binding free energy measures the strength of binding between a ligand and a protein, and an algorithm that would allow its accurate prediction would be a powerful tool for rational drug design. Here we present the development of a new method that allows for the absolute binding free energy of a protein-ligand complex to be calculated from first principles, using a single simulation. Our method involves the use of a novel reaction coordinate that swaps a ligand bound to a protein with an equivalent volume of bulk water. This water-swap reaction coordinate is built using an identity constraint, which identifies a cluster of water molecules from bulk water that occupies the same volume as the ligand in the protein active site. A dual topology algorithm is then used to swap the ligand from the active site with the identified water cluster from bulk water. The free energy is then calculated using replica exchange thermodynamic integration. This returns the free energy change of simultaneously transferring the ligand to bulk water, as an equivalent volume of bulk water is transferred back to the protein active site. This, directly, is the absolute binding free energy. It should be noted that while this reaction coordinate models the binding process directly, an accurate force field and sufficient sampling are still required to allow for the binding free energy to be predicted correctly. In this paper we present the details and development of this method, and demonstrate how the potential of mean force along the water-swap coordinate can be improved by calibrating the soft-core Coulomb and Lennard-Jones parameters used for the dual topology calculation. The optimal parameters were applied to calculations of protein-ligand binding free energies of a neuraminidase inhibitor (oseltamivir), with these results compared to experiment. These results demonstrate that the water-swap coordinate provides a viable and potentially powerful new route for the prediction of protein-ligand binding free energies.

Farnesyltransferase (FTase) is one of the prenyltransferase family enzymes that catalyse the transfer of 15-membered isoprenoid (farnesyl) moiety to the cysteine of CAAX motif-containing proteins including Rho and Ras family of G proteins. Inhibitors of FTase act as drugs for cancer, malaria, progeria and other diseases. In the present investigation, we have developed two structure-based pharmacophore models from protein–ligand complex (3E33 and 3E37) obtained from the protein data bank. Molecular dynamics (MD) simulations were performed on the complexes, and different conformers of the same complex were generated. These conformers were undergone protein–ligand interaction fingerprint (PLIF) analysis, and the fingerprint bits have been used for structure-based pharmacophore model development. The PLIF results showed that Lys164, Tyr166, TrpB106 and TyrB361 are the major interacting residues in both the complexes. The RMSD and RMSF analyses on the MD-simulated systems showed that the absence of FPP in the complex 3E37 has significant effect in the conformational changes of the ligands. During this conformational change, some interactions between the protein and the ligands are lost, but regained after some simulations (after 2 ns). The structure-based pharmacophore models showed that the hydrophobic and acceptor contours are predominantly present in the models. The pharmacophore models were validated using reference compounds, which significantly identified as HITs with smaller RMSD values. The developed structure-based pharmacophore models are significant, and the methodology used in this study is novel from the existing methods (the original X-ray crystallographic coordination of the ligands is used for the model building). In our study, along with the original coordination of the ligand, different conformers of the same complex (protein–ligand) are used. It concluded that the developed methodology is significant for the virtual screening of novel molecules on different targets.

The anticancer effects of arazyme, a bacterial metalloprotease, have been revealed in previous studies. Because of the overexpression of epidermal growth factor receptor (EGFR) in tumor cells, targeting this receptor is one of the approaches to cancer therapy. In the present study, we designed fusion protein by using a non-mitogenic binding sequence of TGFα, arazyme, and a suitable linker. The I-TASSER and Robetta web servers were employed to predict the territory structures of the Arazyme-linker-TGFαL3, and TGFαL3-linker-Arazyme. Then, models were validated by using PROCHECK, ERRAT, ProSA, and MolProbity web servers. After docking to EGFR, Arazyme-linker-TGFαL3 showed a higher binding affinity and was selected to be optimized through 100 ns Molecular dynamic (MD) simulation. Next, the stability of ligand-bound receptor was examined utilizing MD simulation for 100 ns. Furthermore, the binding free energy calculation and free energy decomposition were carried out employing MM-PBSA and MM-GBSA methods, respectively. The root mean square deviation and fluctuation (RMSD, RMSF), the radius of gyration, H-bond, and binding free energy analysis revealed the stability of the complex during simulation time. Finally, linear and conformational epitopes of B cells, as well as MHC class I and MHC class II were predicted by using different web servers. Meanwhile, the potential B cell and T cell epitopes were identified throughout arazyme protein sequence. Collectively, this study suggests a novel chimera protein candidate prevent cancer cells potentially by inducing an immune response and inhibiting cell proliferation. Communicated by Ramaswamy H. Sarma

Carbohydrates are key biological mediators of molecular recognition and signaling processes. In this case study, we explore the ability of absolute binding free energy (ABFE) calculations to predict the affinities of a set of five related carbohydrate ligands for the lectin protein, concanavalin A, ranging from 27-atom monosaccharides to a 120-atom complex-type N-linked glycan core pentasaccharide. ABFE calculations quantitatively rank and estimate the affinity of the ligands in relation to microcalorimetry, with a mean signed error in the binding free energy of -0.63 ± 0.04 kcal/mol. Consequently, the diminished binding efficiencies of the larger carbohydrate ligands are closely reproduced: the ligand efficiency values from isothermal titration calorimetry for the glycan core pentasaccharide and its constituent trisaccharide and monosaccharide compounds are respectively -0.14, -0.22, and -0.41 kcal/mol per heavy atom. ABFE calculations predict these ligand efficiencies to be -0.14 ± 0.02, -0.24 ± 0.03, and -0.46 ± 0.06 kcal/mol per heavy atom, respectively. Consequently, the ABFE method correctly identifies the high affinity of the key anchoring mannose residue and the negligible contribution to binding of both β-GlcNAc arms of the pentasaccharide. While challenges remain in sampling the conformation and interactions of these polar, flexible, and weakly bound ligands, we nevertheless find that the ABFE method performs well for this lectin system. The approach shows promise as a quantitative tool for predicting and deconvoluting carbohydrate-protein interactions, with potential application to design of therapeutics, vaccines, and diagnostics.

The calculation of binding free energies between highly charged species is a major challenge for free energy simulations. In this study, we applied a combination of molecular dynamics simulations and continuum electrostatics, along with normal-mode analysis, to compute the absolute free energies of binding between cobalt (III) hexammine and two RNA fragments for which NMR structures have been determined. The predicted affinities, using the finite-difference Poisson−Boltzmann method with a solute dielectric constant of 1 to treat solvation, were overall underestimated relative to the experimental values. However, internal consistency in the calculated energies was maintained between the different trajectories, and the structures in the simulations gave excellent agreement with NMR data. Various models for obtaining the electrostatic contributions were analyzed, including the effects of solute dielectric constants and van der Waals radii, linear and nonlinear salt contributions, as well as results from gene...

Calculation of the absolute free energy of binding (ΔGbind) for a complex in solution is challenging owing to the need for adequate configurational sampling and an accurate energetic description, typically with a force field (FF). In this study, Monte Carlo (MC) simulations with improved side-chain and backbone sampling are used to assess ΔGbind for the complex of a druglike inhibitor (MIF180) with the protein macrophage migration inhibitory factor (MIF) using free energy perturbation (FEP) calculations. For comparison, molecular dynamics (MD) simulations were employed as an alternative sampling method for the same system. With the OPLS-AA/M FF and CM5 atomic charges for the inhibitor, the ΔGbind results from the MC/FEP and MD/FEP simulations, -8.80 ± 0.74 and -8.46 ± 0.85 kcal/mol, agree well with each other and with the experimental value of -8.98 ± 0.28 kcal/mol. The convergence of the results and analysis of the trajectories indicate that sufficient sampling was achieved for both approaches. Repeating the MD/FEP calculations using current versions of the CHARMM and AMBER FFs led to a 6 kcal/mol range of computed ΔGbind. These results show that calculation of accurate ΔGbind for large ligands is both feasible and numerically equivalent, within error limits, using either methodology.

The recent advances in relative protein-ligand binding free energy calculations have shown the value of alchemical methods in drug discovery. Accurately assessing absolute binding free energies, although highly desired, remains a challenging endeavour, mostly limited to small model cases. Here, we demonstrate accurate first principles based absolute binding free energy estimates for 128 pharmaceutically relevant targets. We use a novel rigorous method to generate protein-ligand ensembles for the ligand in its decoupled state. Not only do the calculations deliver accurate protein-ligand binding affinity estimates, but they also provide detailed physical insight into the structural determinants of binding. We identify subtle rotamer rearrangements between apo and holo states of a protein that are crucial for binding. When compared to relative binding free energy calculations, obtaining absolute binding free energies is considerably more challenging in large part due to the need to explicitly account for the protein in its apo state. In this work we present several approaches to obtain apo state ensembles for accurate absolute ΔG calculations, thus outlining protocols for prospective application of the methods for drug discovery.

Absolute Binding Free Energy Calculations of Drugs to the hERG Channel for the Prediction of Cardiotoxicity

Recently, the massively parallel computation of absolute binding free energy with a well-equilibrated system (MP-CAFEE) has been developed. The present study aimed to determine whether the MP-CAFEE method is useful for drug discovery research. In the drug discovery process, it is important for computational chemists to predict the binding affinity accurately without detailed structural information for protein/ligand complex. We investigated the absolute binding free energies for Poly (ADP-ribose) polymerase-1 (PARP-1)/inhibitor complexes, using the MP-CAFEE method. Although each docking model was used as an input structure, it was found that the absolute binding free energies calculated by MP-CAFEE are well consistent with the experimental ones. The accuracy of this method is much higher than that using molecular mechanics Poisson-Boltzmann/surface area (MM/PBSA). Although the simulation time is quite extensive, the reliable predictor of binding free energies would be a useful tool for drug discovery projects.

Recent advances in relative protein-ligand binding free energy calculations have shown the value of alchemical methods in drug discovery. Accurately assessing absolute binding free energies remains a challenging endeavour, mostly limited to small model cases. We demonstrate accurate absolute binding free energy estimates for 128 pharmaceutically relevant ligands across 7 proteins using a highly parallelizable non-equilibrium method. These calculations also provide detailed physical insight into the structural determinants of binding, identifying subtle rotamer rearrangements between protein apo and holo states that are crucial for binding. The challenge behind absolute binding free energy calculations stems in large part from the need to explicitly account for the protein’s apo state. In this work we present several approaches to obtain apo state ensembles, including a novel rigorous method to generate protein-ligand ensembles for the ligand in its decoupled state. Altogether, we present an effective open-source protocol for prospective application in drug discovery.

Dysregulated activation of the interleukin-21 (IL-21)/IL-21 receptor (IL-21R) signaling pathway is strongly associated with inflammatory and autoimmune disorders, which positions the pathway as a promising therapeutic target. Given the current lack of approved inhibitors or monoclonal antibodies targeting IL-21/IL-21R, we employed a structure-based virtual screening strategy coupled with experimental validation to identify potential IL-21 antagonists from a library of marine natural products provided by TargetMol. Our investigation identified fucoxanthin, a marine-derived carotenoid, as a potent binder to IL-21R, exhibiting a docking score of -8.19kcal/mol. Molecular dynamics simulations further confirmed the stability of the IL-21R-fucoxanthin complex, with a calculated binding free energy (ΔG) of -33.25kcal/mol as determined by MM/PBSA analysis. Importantly, fucoxanthin demonstrated significant immunomodulatory effects by reducing the frequency of key immune cell populations, including CD19+ B cells, memory B cells, and activated follicular helper CD4+ T (Tfh) cells in cultures of peripheral blood mononuclear cells in vitro. These findings suggest that fucoxanthin acts as a potential IL-21 antagonist, offering a novel therapeutic avenue for autoimmune diseases driven by aberrant B- and T-cell differentiation via the IL-21/IL-21R axis.

Relative binding free energy (RBFE) calculations, widely used to predict the potencies of congeneric small molecules binding to a protein receptor, can greatly increase the efficiency of the hit-to-lead and lead optimization stages of the drug discovery process. Traditional RBFE methods, however, cannot be easily applied to small molecules lacking a common core or binding mode, precluding their use in a challenging but crucial component of many drug discovery campaigns. In principle, an absolute binding free energy (ABFE) method can be applied to such molecules, but ABFE often suffers from high computational cost and poor statistical convergence due to the large amount of additional sampling required when compared to RBFE. Here, we introduce core-hopping binding free energy (CBFE) calculations, a computationally efficient framework for the accurate determination of relative binding free energies between small molecules with different cores, leveraging several recently developed techniques such as Alchemical Enhanced Sampling (ACES) with optimized transformation pathways and flexible λ-spacing, as well as λ-dependent Boresch restraints. We benchmark the performance of CBFE across 4 protein systems consisting of 56 small molecules, and find that the results are consistent with RBFE for a congeneric series of ligands and offer considerable improvement in computational cost and precision relative to ABFE results for a series of small molecules with diverse cores and binding modes. All CBFE-related developments are fully implemented in the GPU-accelerated AMBER free energy module (pmemd.cuda) and are available as part of the latest official AMBER release.