Simple Solutions to Energy Calculations

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All substituents involved different modifications of the benzene ring of ligand 1h1q, which forms the central node in our FEP strategy. The low MAE obtained 0. We should stress that in QligFEP no attempts have been made to increase the quality of the ligand parameters generated by any of the automated parameterization methods. In particular, when using either GAFF or CGenFF, quite a few critical warnings were ignored, which in a real prospective application one would certainly optimize, and this could partially account for the lower performance as compared to the results using the OPLS force field.

However, we feel that such an optimization procedure would be beyond the scope of this work, as the aim of the reported tool is to automate FEP calculations, not parameter generation, and the possibility of using different force fields as per the preference and experience of the user. However, as stated before the performance of OPLS3 with a more complex cycle closure strategy only improves slightly in terms of the MAE 0.

All substituents introduced at R are positioned in a solvent exposed cavity on the surface on the protein. The orange line represents the calculated linear equation for the correlation coefficient R 2. The A 2A AR has been widely used by us and others as a test-case for the performance of computational methods in the design of ligands for more complex membrane systems [ 72 ]. This significantly decreases the computational time needed for a simulation as compared to a periodic system of i.

We have shown that calculations performed on larger sphere systems of the same A 2A AR receptor complex result in larger errors, but yield the same average changes in binding free energies [ 12 , 73 ]. On the related A 3 AR, our FEP approach now automated under QligFEP was used to elucidate the role of a nitrogen substitution in the core of antagonists with a pyrimidine scaffold [ 74 ]. The binding mode of this compound, shown in Fig. This includes key hydrogen bonds with N 6.

For comparison, the experimental values are shown together with the values obtained by Lenselink et al. In a first attempt, each of the eight analogs was directly perturbed to the reference ligand 11 note the nomenclature of the compounds following the original report by Minetti et al. Six out of the eight perturbations are well converged, whereas we observed a large SEM over 1. This provides some insight in the convergence limitations of a direct perturbation between two related ligands. In this case, it was evidenced that the phenylethyl 25d or pentyl 25c substitutions are too large to compute reliable relative affinities when directly perturbed to hydrogen To solve this, we created an alternative pathway that involved a smoother annihilation scheme through two intermediate steps linking these ligands to the reference compound 11 , i.

It is worth noting that two perturbations that we accurately model include changes in the core ring 32 and 41 — 11 , which belong to the scaffold hopping modifications that we discuss in more detail below. Thus far, examples presented in this work involved typical lead-optimization strategies, where R-group substituents are introduced to a scaffold with the aim to increase binding affinity.

However, scaffold hopping has been elusive to FEP approaches as it often involves significant amounts of changes in the bond topologies within the ligand series.

Simple Solutions to Energy Calculations, Fourth Edition by Richard R. Vaillencourt - giuliettasprint.konfer.eu

One strategy to overcome this is to change the asymptotic nature of the bond by introducing a softcore bond potential [ 78 ]. Within the QligFEP dual-topology framework, we introduce an alternative approach, since no bonded terms are altered within the transition of the ligand pair, even if the bond topology changes.

To test the applicability of this approach in a typical scaffold-hopping case, we performed a total of six perturbations between five inhibitors of the Checkpoint kinase 1 Chk1, see Fig. Even if the changes in affinity within the series are relatively small which is typically the case in a successful scaffold hoping project, i. This shows that the practical advantages of a dual topology approach in scaffold hopping do not cause a lower performance as compared to single topology approaches.

Core of the scaffold of the Chk1 inhibitor series, showing the variable position explored by scaffold hopping with the five different R-group modifications bottom. We present QligFEP, an automated workflow to setup, run and analyze ligand free energy perturbation calculations within a structure-based drug design framework.

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The workflow is built as an API that interacts with the open-source software package Q. It aims to automate the tedious process of creating input and parameters files and to facilitate a robust setup to perform free energy calculations between a given pair of ligands to calculate e. We show how this approach can be scaled up to perform pair comparisons in a high-throughput fashion for a versatile set of ligands and receptors, which to the best of our knowledge is achievable with a limited amount of software packages [ 9 , 29 , 49 ].

Given a dataset with a number l of ligands, one can eventually perform all possible pair comparisons, which is proportional to l 2. This brings up the question on how to optimize the design of the FEP strategy such that a selected sample of pair comparisons l i , l j represent the spectrum of relative affinities to cover the whole dataset whilst minimizing the structural changes involved in order to ensure sufficient convergence.

One way to approach this problem is through the use of MCS algorithms, further combined with the idea of cycle closures, to validate the FEP pathways i. While for big datasets this automated approach is a useful solution, in most cases one can design pathways based on basic medicinal chemistry knowledge, and assess the feasibility of this design solely based on the convergence achieved. This is nicely illustrated in this work by the calculations performed on the A 2A AR antagonists set.

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The initial design of the FEP pathway involved the calculation of the affinities of each compound relative to a single reference compound. This is, in our opinion, the recommended approach in a lead optimization project, where typically one only knows the affinity of a reference compound and variations of one or several substituents are designed.

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In this case, two perturbations showed low convergence, detected by an abnormally high SEM within the replicate simulations of the system. We redesigned the strategy to circumvent this issue, involving a cycle closure defined by four compounds within the dataset. Third, the simulation times reported are 10 times longer see methods section for a single simulation per ligand pair. Taken all together, QligFEP provides an alternative for efficient FEP simulations, at a substantial reduction of the computational cost as compared with existing commercial software.

In other words, we show an alternative to a more exhaustive cycle closure, which is based on starting with a simple FEP design based on the MCS idea, detect potential problematic pairs based on convergence using the SEM as a metric and iterative processing of the obtained data if needed. Another advantage of our approach is the implementation of a dual topology representation, which allows the comparison of molecules with unrelated topologies. This is a particularly interesting approach in, for instance, scaffold hopping where one wants to normally compare different chemical scaffolds.

In a single topology framework, where a one-to-one mapping of atoms between the two states is used, this potentially includes the transformation of bonded terms to represent the change in bond topology. In contrast, with QligFEP this problem can be circumvented, whilst still achieving excellent correlation with experiment.

Our initial results are promising, and the next direction in our research will be to apply the QligFEP dual topology approach the binding affinity prediction of fragment-like compounds, which involves comparing different chemical scaffolds where even potential multiple binding modes can be assessed.

QligFEP provides a versatile, robust and accurate framework to routinely perform FEP calculations in structure-based ligand design projects. We herein show the performance on a diverse benchmark set of systems and ligands, showing agreement between experiment and calculations similar to that of other state-of-the-art methods.

Additionally, our package already works with a number of standard force fields, whilst the implementation of additional force fields is currently under development. The benchmark test sets included in this study are part of the tutorials and provide a useful start for users with limited training in free energy calculations. J Comput Chem — Hornak V, Abel R, Okur A et al Comparison of multiple amber force fields and development of improved protein backbone parameters. Proteins Struct Funct Genet — J Chem Theory Comput — Nucleic Acids Res W—W Biophys J — Comput Chem — Cournia Z, Allen B, Sherman W Relative binding free energy calculations in drug discovery: recent advances and practical considerations.

J Chem Inf Model — J Comput Aided Mol Des — Phys Today PLoS Comput Biol e Chem Commun — Reymond JL The chemical space project. Acc Chem Res — Wang L, Wu Y, Deng Y et al Accurate and reliable prediction of relative ligand binding potency in prospective drug discovery by way of a modern free-energy calculation protocol and force field. J Am Chem Soc — Christ CD, Fox T Accuracy assessment and automation of free energy calculations for drug design.

Michel J, Essex JW Prediction of protein-ligand binding affinity by free energy simulations: assumptions, pitfalls and expectations. Marelius J, Kolmodin K, Feierberg I et al Q: a molecular dynamics program for free energy calculations and empirical valence bond simulations in biomolecular systems. J Mol Graph Model — Bauer P, Barrozo A, Purg M et al Q6: a comprehensive toolkit for empirical valence bond and related free energy calculations. Lee F, Warshel A A local reaction field method for fast evaluation of long-range electrostatic interactions in molecular simulations.

J Chem Phys — Proc Natl Acad Sci — Biochemistry — ACS Omega — Design, synthesis, and pharmacological characterization. J Med Chem — Purg M, Bauer P qtools v0. J Am Chem Soc U Methods Mol Biol — J Phys Chem B — Adv Protein Chem — Zwanzig RW High-temperature equation of state by a perturbation method. Nonpolar gases. J Chem Phys Kirkwood JG Statistical mechanics of fluid mixtures.

J Comput Phys — Metron — J Mach Learn Res — Madhavi Sastry G, Adzhigirey M, Day T et al Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments. Biochem Soc Trans — King G, Warshel A A surface constrained all-atom solvent model for effective simulations of polar solutions. Lee FS, Warshel A A local reaction field method for fast evaluation of long-range electrostatic interactions in molecular simulations.

Chem Phys Lett — J Phys Chem — Zhao H Scaffold selection and scaffold hopping in lead generation: a medicinal chemistry perspective. Drug Discov Today — Adv Drug Deliv Rev — Curr Opin Struct Biol — Zhang H, Jiang Y, Yan H et al Free-energy calculations of ionic hydration consistent with the experimental hydration free energy of the proton. J Phys Chem Lett — J Phys Chem A — Kelly CP, Cramer CJ, Truhlar DG Aqueous solvation free energies of ions and ion-water clusters based on an accurate value for the absolute aqueous solvation free energy of the proton.

Minnesota Solvation Database version J Chem Eng Data — Curr Top Med Chem — Azuaje J, Jespers W, Yaziji V et al Effect of nitrogen atom substitution in A 3 adenosine receptor binding: N - 4,6-diarylpyridinyl acetamides as potent and selective antagonists. Science — Trends Pharmacol Sci — Drug Discov Today Technol — Download references. All authors read and approved the final manuscript.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Reprints and Permissions. Search all BMC articles Search. Abstract The process of ligand binding to a biological target can be represented as the equilibrium between the relevant solvated and bound states of the ligand.

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Introduction Calculating physicochemical properties of drug like molecules, such as the solvation free energies or the binding affinities for biological targets, has been a longstanding challenge to computational chemists. It connects based by sure communication, a nstigt to Learn others shown, team, cookies of specific theorists, or Sign to forget additional millions at the same support.

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Simple Solutions to Energy Calculations Fifth Edition

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