Host−guest systems are widely used in benchmarks as model systems to improve computational methods for absolute binding free energy predictions. Recent advances in sampling algorithms for alchemical free energy calculations and the increase in computational power have made their binding affinity prediction primarily dependent on the quality of the force field.Here, we propose a new methodology to derive the atomic charges of host−guest systems based on quantum mechanics/molecular mechanics calculations and minimal basis iterative stockholder (MBIS) partitioning of the polarized electron density. A newly developed interface between the OpenMM and ORCA software packages provides D-MBIS charges that represent the guest's average electrostatic interactions in the hosts or the solvent. The simulation workflow also calculates the average energy required to polarize the guest in the bound and unbound state. Alchemical free energy calculations using the general Amber force field parameters with D-MBIS charges improve the binding affinity prediction of six guests bound to two octa acid hosts compared to the AM1-BCC charge set after correction with the average energetic polarization cost. This correction originates from the difference in potential energy that is required to polarize the guest in the bound and unbound state and contributes significantly to the binding affinity of anionic guests.

Binding affinity prediction by means of computer simulation has been increasingly incorporated in drug discovery projects. Its wide application, however, is limited by the prediction accuracy of the free energy calculations. The main error sources are force fields used to describe molecular interactions and incomplete sampling of the configurational space. Organic host–guest systems have been used to address force field quality because they share similar interactions found in ligands and receptors, and their rigidity facilitates configurational sampling. Here, we test the binding free energy prediction accuracy for 14 guests with an aromatic or adamantane core and the CB7 host using molecular electron density derived nonbonded force field parameters. We developed a computational workflow written in Python to derive atomic charges and Lennard-Jones parameters with the Minimal Basis Iterative Stockholder method using the polarized electron density of several configurations of each guest in the bound and unbound states. The resulting nonbonded force field parameters improve binding affinity prediction, especially for guests with an adamantane core in which repulsive exchange and dispersion interactions to the host dominate.

<div> <div> <div> <p>Host-guest systems are widely used in benchmarks as model systems to improve computational methods for absolute binding free energy predictions. Recent advances in sampling algorithms for alchemical free energy calculations and the increase in computational power have made their binding affinity prediction primarily dependent on the quality of the force field. Here, we propose a new methodology to derive the atomic charges of host-guest systems based on QM/MM calculations and the MBIS partitioning of the polarized electron density. A newly developed interface between the OpenMM and ORCA software package provides D-MBIS charges that best represent the guest’s average electrostatic interactions in the hosts or the solvent. The simulation workflow also calculates the average energy required to polarize the guest in the bound and unbound state. Alchemical free energy calculations using the GAFF force field parameters with D-MBIS charges improve the binding affinity prediction of six guests bound to two octa-acid hosts compared to the AM1-BCC charge set after correction with the average energetic polarization cost. This correction results from the difference in the energetic polarization cost between the bound and unbound state and contributes significantly to the binding affinity of anionic guests. </p></div></div></div><div><div><div> </div> </div> </div>

IOData is a free and open‐source Python library for parsing, storing, and converting various file formats commonly used by quantum chemistry, molecular dynamics, and plane‐wave density‐functional‐theory software programs. In addition, IOData supports a flexible framework for generating input files for various software packages. While designed and released for stand‐alone use, its original purpose was to facilitate the interoperability of various modules in the HORTON and ChemTools software packages with external (third‐party) molecular quantum chemistry and solid‐state density‐functional‐theory packages. IOData is designed to be easy to use, maintain, and extend; this is why we wrote IOData in Python and adopted many principles of modern software development, including comprehensive documentation, extensive testing, continuous integration/delivery protocols, and package management. This article is the official release note of the IOData library.

TNFα is a pro-inflammatory cytokine that is a therapeutic target for inflammatory autoimmune disorders. Thus, TNFα antagonists are successfully used for the treatment of these disorders. Here, new association patterns of rhTNFα and its antagonists Adalimumab and Etanercept are disclosed. Active rhTNFα was purified by IMAC from the soluble fraction of transformed Escherichia coli. Protein detection was assessed by SDS–PAGE and Western blot. The KD values for rhTNFα interactions with their antagonists were obtained by non-competitive ELISA and by microscale thermophoresis (MST). Molecular sizes of the complexes were evaluated by size-exclusion chromatography-high performance liquid chromatography (SEC-HPLC). Surprisingly, both antagonists recognized the monomeric form of rhTNFα under reducing and non-reducing conditions, indicating unexpected bindings of the antagonists to linear epitopes and to rhTNFα monomers. For the first time, the interactions of rhTNFα with Adalimumab and Etanercept were assessed by MST, which allows evaluating molecular interactions in solution with a wide range of concentrations. Biphasic binding curves with low and high KD values (&lt;10−9 M and &gt;10−8 M) were observed during thermophoresis experiments, suggesting the generation of complexes with different stoichiometry, which were confirmed by SEC-HPLC. Our results demonstrated the binding of TNFα-antagonists with rhTNFα monomers and linear epitopes. Also, complexes of high molecular mass were observed. This pioneer investigation constitutes valuable data for future approaches into the study of the interaction mechanism of TNFα and its antagonists.