Electrostatically embedded symmetry-adapted perturbation theory

Symmetry-adapted perturbation theory (SAPT) is an ab initio approach that directly computes noncovalent interaction energies in terms of electrostatics, exchange repulsion, induction/polarization, and London dispersion components. Due to its high computational scaling, routine applications of even the lowest order of SAPT are typically limited to a few hundred atoms. To address this limitation, we report here the addition of electrostatic embedding to the SAPT (EE-SAPT) and ISAPT (EE-ISAPT) methods. We illustrate the embedding scheme using water trimer as a prototype example. Then, we show that EE-SAPT/EE-ISAPT can be applied for efficiently and accurately computing noncovalent interactions in large systems, including solvated dimers and protein-ligand systems. In the latter application, particular care must be taken to properly handle the quantum mechanics/molecular mechanics boundary when it cuts covalent bonds. We investigate various schemes for handling charges near this boundary and demonstrate which are most effective in the context of charge-embedded SAPT.

Kohn-Sham fragment energy decomposition analysis

We introduce the concept of Kohn-Sham fragment localized molecular orbitals (KS-FLMOs), which are Kohn-Sham molecular orbitals (MOs) localized in specific fragments constituting a generic molecular system. In detail, we minimize the local electronic energies of various fragments, while maximizing the repulsion between them, resulting in the effective localization of the MOs. We use the developed KS-FLMOs to propose a novel energy decomposition analysis, which we name Kohn-Sham fragment energy decomposition analysis, which allows for rationalizing the main non-covalent interactions occurring in interacting systems both in vacuo and in solution, providing physical insights into non-covalent interactions. The method is validated against state-of-the-art energy decomposition analysis techniques and with high-level calculations.

A physics-aware neural network for protein-ligand interactions with quantum chemical accuracy

Quantifying intermolecular interactions with quantum chemistry (QC) is useful for many chemical problems, including understanding the nature of protein-ligand interactions. Unfortunately, QC computations on protein-ligand systems are too computationally expensive for most use cases. The flourishing field of machine-learned (ML) potentials is a promising solution, but it is limited by an inability to easily capture long range, non-local interactions. In this work we develop an atomic-pairwise neural network (AP-Net) specialized for modeling intermolecular interactions. This model benefits from a number of physical constraints, including a two-component equivariant message passing neural network architecture that predicts interaction energies via an intermediate prediction of monomer electron densities. The AP-Net model is trained on a comprehensive dataset composed of paired ligand and protein fragments. This model accurately predicts QC-quality interaction energies of protein-ligand systems at a computational cost reduced by orders of magnitude. Applications of the AP-Net model to molecular crystal structure prediction are explored, as well as limitations in modeling highly polarizable systems.

An efficient method by combining different basis sets and SAPT levels

In symmetry-adapted perturbation theory (SAPT), accurate calculations on non-covalent interaction (NCI) for large complexes with more than 50 atoms are time-consuming using large basis sets. More efficient ones with smaller basis sets usually result in poor prediction in terms of dispersion and overall energies. In this study, we propose two composite methods with baseline calculated at SAPT2/aug-cc-pVDZ and SAPT2/aug-cc-pVTZ with dispersion term corrected at SAPT2+ level using bond functions and smaller basis set with δ MP2 corrections respectively. Benchmark results on representative NCI data sets, such as S22, S66, and so forth, show significant improvements on the accuracy compared to the original SAPT Silver standard and comparable to SAPT Gold standard in some cases with much less computational cost.

Detangling Solvatochromic Effects by the Effective Fragment Potential Method

Understanding molecular interactions in complex systems opens avenues for the efficient design of new materials with target properties. Energy decomposition methods provide a means to obtain a detailed picture of intermolecular interactions. This work introduces a molecular modeling approach for decomposing the solvatochromic shifts of the electronic excited states into the contributions of the individual molecular fragments of the environment surrounding the chromophore. The developed approach is implemented for the QM/EFP (quantum mechanics/effective fragment potential) model that provides a rigorous first-principles-based description of the electronic states of the chromophores in complex polarizable environments. On the example of two model systems, water pentamer and hydrated uracil, we show how the decomposition of the solvatochromic shifts into the contributions of individual solvent water molecules provides a detailed picture of the intermolecular interactions in the ground and excited states of these systems. The analysis also demonstrates the nonadditivity of solute-solvent interactions and the significant contribution of solute polarization to the total values of solvatochromic shifts.

A Machine Learning Force Field for Bio-Macromolecular Modeling Based on Quantum Chemistry-Calculated Interaction Energy Datasets

Accurate energy data from noncovalent interactions are essential for constructing force fields for molecular dynamics simulations of bio-macromolecular systems. There are two important practical issues in the construction of a reliable force field with the hope of balancing the desired chemical accuracy and working efficiency. One is to determine a suitable quantum chemistry level of theory for calculating interaction energies. The other is to use a suitable continuous energy function to model the quantum chemical energy data. For the first issue, we have recently calculated the intermolecular interaction energies using the SAPT0 level of theory, and we have systematically organized these energies into the ab initio SOFG-31 (homodimer) and SOFG-31-heterodimer datasets. In this work, we re-calculate these interaction energies by using the more advanced SAPT2 level of theory with a wider series of basis sets. Our purpose is to determine the SAPT level of theory proper for interaction energies with respect to the CCSD(T)/CBS benchmark chemical accuracy. Next, to utilize these energy datasets, we employ one of the well-developed machine learning techniques, called the CLIFF scheme, to construct a general-purpose force field for biomolecular dynamics simulations. Here we use the SOFG-31 dataset and the SOFG-31-heterodimer dataset as the training and test sets, respectively. Our results demonstrate that using the CLIFF scheme can reproduce a diverse range of dimeric interaction energy patterns with only a small training set. The overall errors for each SAPT energy component, as well as the SAPT total energy, are all well below the desired chemical accuracy of ~1 kcal/mol.

Intermolecular Non-Bonded Interactions from Machine Learning Datasets

Accurate determination of intermolecular non-covalent-bonded or non-bonded interactions is the key to potentially useful molecular dynamics simulations of polymer systems. However, it is challenging to balance both the accuracy and computational cost in force field modelling. One of the main difficulties is properly representing the calculated energy data as a continuous force function. In this paper, we employ well-developed machine learning techniques to construct a general purpose intermolecular non-bonded interaction force field for organic polymers. The original ab initio dataset SOFG-31 was calculated by us and has been well documented, and here we use it as our training set. The CLIFF kernel type machine learning scheme is used for predicting the interaction energies of heterodimers selected from the SOFG-31 dataset. Our test results show that the overall errors are well below the chemical accuracy of about 1 kcal/mol, thus demonstrating the promising feasibility of machine learning techniques in force field modelling.

"Magic Chloro": Profound Effects of the Chlorine Atom in Drug Discovery

Chlorine is one of the most common atoms present in small-molecule drugs beyond carbon, hydrogen, nitrogen, and oxygen. There are currently more than 250 FDA-approved chlorine-containing drugs, yet the beneficial effect of the chloro substituent has not yet been reviewed. The seemingly simple substitution of a hydrogen atom (R = H) with a chlorine atom (R = Cl) can result in remarkable improvements in potency of up to 100,000-fold and can lead to profound effects on pharmacokinetic parameters including clearance, half-life, and drug exposure in vivo. Following the literature terminology of the "magic methyl effect" in drugs, the term "magic chloro effect" has been coined herein. Although reports of 500-fold or 1000-fold potency improvements are often serendipitous discoveries that can be considered "magical" rather than planned, hypotheses made to explain the magic chloro effect can lead to lessons that accelerate the cycle of drug discovery.

The pyridazine heterocycle in molecular recognition and drug discovery

The pyridazine ring is endowed with unique physicochemical properties, characterized by weak basicity, a high dipole moment that subtends π-π stacking interactions and robust, dual hydrogen-bonding capacity that can be of importance in drug-target interactions. These properties contribute to unique applications in molecular recognition while the inherent polarity, low cytochrome P450 inhibitory effects and potential to reduce interaction of a molecule with the cardiac hERG potassium channel add additional value in drug discovery and development. The recent approvals of the gonadotropin-releasing hormone receptor antagonist relugolix (24) and the allosteric tyrosine kinase 2 inhibitor deucravacitinib (25) represent the first examples of FDA-approved drugs that incorporate a pyridazine ring. In this review, the properties of the pyridazine ring are summarized in comparison to the other azines and its potential in drug discovery is illustrated through vignettes that explore applications that take advantage of the inherent physicochemical properties as an approach to solving challenges associated with candidate optimization.

Graphical Abstract

Range-dependence of two-body intermolecular interactions and their energy components in molecular crystals

Routinely assessing the stability of molecular crystals with high accuracy remains an open challenge in the computational sciences. The many-body expansion decomposes computation of the crystal lattice energy into an embarrassingly parallel collection of computations over molecular dimers, trimers, and so forth, making quantum chemistry techniques tractable for many crystals of small organic molecules. By examining the range-dependence of different types of energetic contributions to the crystal lattice energy, we can glean qualitative understanding of solid-state intermolecular interactions as well as practical, exploitable reductions in the number of computations required for accurate energies. Here, we assess the range-dependent character of two-body interactions of 24 small organic molecular crystals using the physically interpretable components from symmetry-adapted perturbation theory (electrostatics, exchange repulsion, induction/polarization, and London dispersion). We also examine correlations between the convergence rates of electrostatics and London dispersion terms with molecular dipole moments and polarizabilities, to provide guidance for estimating convergence rates in other molecular crystals.

Comprehensive Basis-Set Testing of Extended Symmetry-Adapted Perturbation Theory and Assessment of Mixed-Basis Combinations to Reduce Cost

Hybrid or "extended" symmetry-adapted perturbation theory (XSAPT) replaces traditional SAPT's treatment of dispersion with better performing alternatives while at the same time extending two-body (dimer) SAPT to a many-body treatment of polarization using a self-consistent charge embedding procedure. The present work presents a systematic study of how XSAPT interaction energies and energy components converge with respect to the choice of Gaussian basis set. Errors can be reduced in a systematic way using correlation-consistent basis sets, with aug-cc-pVTZ results converged within <0.1 kcal/mol. Similar (if slightly less systematic) behavior is obtained using Karlsruhe basis sets at much lower cost, and we introduce new versions with limited augmentation that are even more efficient. Pople-style basis sets, which are more efficient still, often afford good results if a large number of polarization functions are included. The dispersion models used in XSAPT afford much faster basis-set convergence as compared to the perturbative description of dispersion in conventional SAPT, meaning that "compromise" basis sets (such as jun-cc-pVDZ) are no longer required and benchmark-quality results can be obtained using triple-ζ basis sets. The use of diffuse functions proves to be essential, especially for the description of hydrogen bonds. The "δ(Hartree-Fock)" correction for high-order induction can be performed in double-ζ basis sets without significant loss of accuracy, leading to a mixed-basis approach that offers 4× speedup over the existing (cubic scaling) XSAPT approach.

Synthesis of Substituted -N-(5-((7-Methyl-2-Oxo-2H-Chromen-4-yl)- Methyl)-1,3,4-Thiadiazol-2-yl)-Benzamide Derivatives Using TBTU as Coupling Agent and their Evaluation for Anti Tubercular Activity

Abstract:

Tuberculosis remains a highly infectious disease across the world. In the identification of new antitubercular agents, coumarin clubbed thiadiazole amides have been synthesized and evaluated for in vitro antitubercular activity. Owing to the growing concern of chemicals and their impact on the environment, greener and faster reaction conditions needed to be incorporated. Therefore, we used TBTU as a coupling reagent for efficient and facile synthesis of substituted-N-(5-((7-methyl-2-oxo-2Hchromes- 4-yl)-methyl)-1,3, 4-thiadiazol-2-yl)-benzamide 4a-j with good yields up to 95% in mild reaction conditions. All the synthesized compounds were evaluated in vitro for anti-tubercular activity against the H37Rv strain of M. tuberculosis. Compounds 4c, 4d, and 4f were found active at 12.5 μg/mL against M. tb H37Rv. Electron withdrawing substituents present on aromatic side chains showed promising anti-tubercular activity.

Optimized damping parameters for empirical dispersion corrections to symmetry-adapted perturbation theory

Symmetry-adapted perturbation theory (SAPT) has become an invaluable tool for studying the fundamental nature of non-covalent interactions by directly computing the electrostatics, exchange (steric) repulsion, induction (polarization), and London dispersion contributions to the interaction energy using quantum mechanics. Further application of SAPT is primarily limited by its computational expense, where even its most affordable variant (SAPT0) scales as the fifth power of system size [O(N⁵)] due to the dispersion terms. The algorithmic scaling of SAPT0 is reduced from O(N⁵)→O(N⁴) by replacing these terms with the empirical D3 dispersion correction of Grimme and co-workers, forming a method that may be termed SAPT0-D3. Here, we optimize the damping parameters for the -D3 terms in SAPT0-D3 using a much larger training set than has previously been considered, namely, 8299 interaction energies computed at the complete-basis-set limit of coupled cluster through perturbative triples [CCSD(T)/CBS]. Perhaps surprisingly, with only three fitted parameters, SAPT0-D3 improves on the accuracy of SAPT0, reducing mean absolute errors from 0.61 to 0.49 kcal mol^-1 over the full set of complexes. Additionally, SAPT0-D3 exhibits a nearly 2.5× speedup over conventional SAPT0 for systems with ∼300 atoms and is applied here to systems with up to 459 atoms. Finally, we have also implemented a functional group partitioning of the approach (F-SAPT0-D3) and applied it to determine important contacts in the binding of salbutamol to G-protein coupled β₁-adrenergic receptor in both active and inactive forms. SAPT0-D3 capabilities have been added to the open-source Psi4 software.

CLIFF: A component-based, machine-learned, intermolecular force field

Computation of intermolecular interactions is a challenge in drug discovery because accurate ab initio techniques are too computationally expensive to be routinely applied to drug-protein models. Classical force fields are more computationally feasible, and force fields designed to match symmetry adapted perturbation theory (SAPT) interaction energies can remain accurate in this context. Unfortunately, the application of such force fields is complicated by the laborious parameterization required for computations on new molecules. Here, we introduce the component-based machine-learned intermolecular force field (CLIFF), which combines accurate, physics-based equations for intermolecular interaction energies with machine-learning models to enable automatic parameterization. The CLIFF uses functional forms corresponding to electrostatic, exchange-repulsion, induction/polarization, and London dispersion components in SAPT. Molecule-independent parameters are fit with respect to SAPT2+(3)δMP2/aug-cc-pVTZ, and molecule-dependent atomic parameters (atomic widths, atomic multipoles, and Hirshfeld ratios) are obtained from machine learning models developed for C, N, O, H, S, F, Cl, and Br. The CLIFF achieves mean absolute errors (MAEs) no worse than 0.70 kcal mol^-1 in both total and component energies across a diverse dimer test set. For the side chain-side chain interaction database derived from protein fragments, the CLIFF produces total interaction energies with an MAE of 0.27 kcal mol^-1 with respect to reference data, outperforming similar and even more expensive methods. In applications to a set of model drug-protein interactions, the CLIFF is able to accurately rank-order ligand binding strengths and achieves less than 10% error with respect to SAPT reference values for most complexes.

Effective Fragment Potentials for Flexible Molecules: Transferability of Parameters and Amino Acid Database

An accurate but efficient description of noncovalent interactions is a key to predictive modeling of biological and materials systems. The effective fragment potential (EFP) is an ab initio-based force field that provides a physically meaningful decomposition of noncovalent interactions of a molecular system into Coulomb, polarization, dispersion, and exchange-repulsion components. An EFP simulation protocol consists of two steps, preparing parameters for molecular fragments by a series of ab initio calculations on each individual fragment, and calculation of interaction energy and properties of a total molecular system based on the prepared parameters. As the fragment parameters (distributed multipoles, polarizabilities, localized wave function, etc.) depend on a fragment geometry, straightforward application of the EFP method requires recomputing parameters of each fragment if its geometry changes, for example, during thermal fluctuations of a molecular system. Thus, recomputing fragment parameters can easily become both computational and human bottlenecks and lead to a loss of efficiency of a simulation protocol. An alternative approach, in which fragment parameters are adjusted to different fragment geometries, referred to as "flexible EFP", is explored here. The parameter adjustment is based on translations and rotations of local coordinate frames associated with fragment atoms. The protocol is validated on extensive benchmark of amino acid dimers extracted from molecular dynamics snapshots of a cryptochrome protein. A parameter database for standard amino acids is developed to automate flexible EFP simulations in proteins. To demonstrate applicability of flexible EFP in large-scale protein simulations, binding energies and vertical electron ionization and electron attachment energies of a lumiflavin chromophore of the cryptochrome protein are computed. The results obtained with flexible EFP are in a close agreement with the standard EFP procedure but provide a significant reduction in computational cost.

Description of Chiral Complexes within Functional-Group Symmetry-Adapted Perturbation Theory-The Case of (S/R)-Carvone with Derivatives of (-)-Menthol

Symmetry-adapted perturbation theory (SAPT) and functional-group SAPT (F-SAPT) are applied to examine differences in interaction energies of diastereoisomeric complexes of two chiral molecules of natural origin: (S/R)-carvone with (-)-menthol. The study is extended by including derivatives of menthol with its hydroxy group exchanged by another functional group, thus examining the substituent effect of the interaction and the interaction differences between diastereoisomers. The partitioning of the interaction energy into functional-group components allows one to explain this phenomenon by the mutual cancellation of attractive and repulsive interactions between functional groups. In some cases, one can identify dominant chiral interactions between groups of atoms of carvone and menthol derivatives, while in many other instances, no major interaction can be distinguished and the net chiral difference results from subtle near cancellation of several smaller terms. Our results indicate that the F-SAPT method can be faithfully utilized for such analyses.

Microwave-assisted One-pot Synthesis of Amide Bond using WEB

Background:

Amide bond plays a key role in medicinal chemistry, and the analysis of bioactive molecular database revealed that the carboxamide group appears in more than 25% of the existing database drugs. Typically amide bonds are formed from the union of carboxylic acid and amine; however, the product formation does not occur spontaneously. Several synthetic methods have been reported for amide bond formation in literature. Present work demonstrated simple and eco-friendly amide bond formation using carboxylic acid and primary amines through in situ generation of O-acylurea. The reaction was found to be more efficient, faster reaction rate; simple work-up gave pure compound isolation in moderate to excellent yield using microwave irradiation as compared to conventional heating.

Methods:

Developed one-pot synthesis of amide compounds using agro-waste derived greener catalyst under microwave irradiation.

Results:

Twenty amide bond containing organic compounds are synthesized from carboxylic acid with primary amine catalyzed by agro-waste derived medium under microwave irradiation. First, the reaction involved carboxylic acid activation using EDC.HCl, which is the required base for the neutralization and coupling. The method employed natural agro-waste derived from banana peel ash (WEB) for the coupling gave target amide product without the use of an external organic or inorganic base.

Conclusion:

In the present work, we demonstrated that agro-waste extract is an alternative greener catalytic medium for the condensation of organic carboxylic acid and primary amine under microwave irradiation. The method found several advantages compared to reported methods like solventfree, non-toxic, cheaper catalyst, and simple reaction condition. The final isolated product achieved chromatographically pure by simple recrystallization and did not require further purification.

Urea-aromatic interactions in biology

Noncovalent interactions are key determinants in both chemical and biological processes. Among such processes, the hydrophobic interactions play an eminent role in folding of proteins, nucleic acids, formation of membranes, protein-ligand recognition, etc.. Though this interaction is mediated through the aqueous solvent, the stability of the above biomolecules can be highly sensitive to any small external perturbations, such as temperature, pressure, pH, or even cosolvent additives, like, urea-a highly soluble small organic molecule utilized by various living organisms to regulate osmotic pressure. A plethora of detailed studies exist covering both experimental and theoretical regimes, to understand how urea modulates the stability of biological macromolecules. While experimentalists have been primarily focusing on the thermodynamic and kinetic aspects, theoretical modeling predominantly involves mechanistic information at the molecular level, calculating atomistic details applying the force field approach to the high level electronic details using the quantum mechanical methods. The review focuses mainly on examples with biological relevance, such as (1) urea-assisted protein unfolding, (2) urea-assisted RNA unfolding, (3) urea lesion interaction within damaged DNA, (4) urea conduction through membrane proteins, and (5) protein-ligand interactions those explicitly address the vitality of hydrophobic interactions involving exclusively the urea-aromatic moiety.

On the applicability of functional-group symmetry-adapted perturbation theory and other partitioning models for chiral recognition - the case of popular drug molecules interacting with chiral phases

The applicability of symmetry-adapted perturbation theory (SAPT) and functional-group SAPT (F-SAPT) to study chiral recognition is investigated on an example of three popular chiral drug molecules: ibuprofen, norepinephrine, and baclofen, interacting with phenethylamine or proline - two molecules that are often used as chiral phases in chromatography. The comparison of the F-SAPT with the interacting quantum atoms (IQA) approach is also provided. Accurate estimation of energetic differences of the non-covalent intermolecular complexes composed of two chiral molecules is a necessary prerequisite for the possibility of a prediction of the chiral recognition. The SAPT method with interacting molecules described on the density functional theory level provides accurate total interaction energies, while the F-SAPT approach is the most useful in determining which functional groups are responsible for strengthening or weakening of the interaction between chiral molecules. The largest difference in the interaction energies has been calculated for the baclofen-phenethylamine and norepinephrine-phenethylamine pairs, while the smallest for the ibuprofen-proline and baclofen-proline ones. In most cases, the intermolecular interaction is found to be composed of a strong directional hydrogen bond, which was stabilized by two or more weaker non-covalent interactions between groups (in accordance with the phenomological three-point rule), but in several cases more subtle factors are responsible for larger stability of one diastereoisomer, like the stabilization of the conformation involving two noninteracting functional groups attached to a chiral atom through intramolecular attraction. Additionally, the simulated IR spectra were analyzed for all pairs of diastereoisomeric complexes and the red- and blue-shifts of characteristic bond vibrations were discussed in the context of inter-group interactions.

Symmetry-Adapted Perturbation Theory Decomposition of the Reaction Force: Insights into Substituent Effects Involved in Hemiacetal Formation Mechanisms

The decomposition of the reaction force based on symmetry-adapted perturbation theory (SAPT) has been proposed. This approach was used to investigate the substituent effects along the reaction coordinate pathway for the hemiacetal formation mechanism between methanol and substituted aldehydes of the form CX₃CHO (X = H, F, Cl, and Br), providing a quantitative evaluation of the reaction-driving and reaction-retarding force components. Our results highlight the importance of more favorable electrostatic and induction effects in the reactions involving halogenated aldehydes that leads to lower activation energy barriers. These substituent effects are further elucidated by applying the functional-group partition of symmetry-adapted perturbation theory (F-SAPT). The results show that the reaction is largely driven by favorable direct noncovalent interactions between the CX₃ group on the aldehyde and the OH group on methanol.

Analysis of transition state stabilization by non-covalent interactions in organocatalysis: application of atomic and functional-group partitioned symmetry-adapted perturbation theory to the addition of organoboron reagents to fluoroketones

This work seeks to apply symmetry-adapted perturbation theory (SAPT) to the recent study of Hoveyda and co-workers [K. A. Lee et al., Nat. Chem. 2016, 8, 768] where an allyl addition to a ketone became enantioselective when the ketone was fluorinated. Through the application of atomic SAPT (A-SAPT) and functional-group SAPT (F-SAPT), the non-covalent interactions between specific atoms and functional groups in the transition states associated with the fluoroketone reactions can be quantified. Our A-SAPT analysis confirms that a HF contact thought to enhance stereoselectivity shows a strong preference for one of the transition states leading to the experimentally observed product enantiomer. Other key atom-atom contacts invoked to rationalize relative transition state energies are also found to behave as expected based on chemical intuition and contact distances. On the other hand, hypothesized steric clashes between substrate phenyl or ortho-methyl phenyl groups and the catalyst are not supported by F-SAPT computations, and indeed, these are actually favorable π-π interactions.

Assessment of Density Functional Methods for Geometry Optimization of Bimolecular van der Waals Complexes

We explore the suitability of three popular density functionals (B97-D3, B3LYP-D3, M05-2X) for producing accurate equilibrium geometries of van der Waals (vdW) complexes with diverse binding motifs. For these functionals, optimizations using Dunning's aug-cc-pVDZ basis set best combine accuracy and a reasonable computational expense. Each DFT/aug-cc-pVDZ combination produces optimized equilibrium geometries for 21 small vdW complexes of organic molecules (up to four non-hydrogen atoms total) that agree with high-level CCSD(T)/CBS reference geometries to within ±0.1 Å for the averages of the center-of-mass displacement and the mean least root-mean-squared displacement. The DFT/aug-cc-pVDZ combinations are also able to reproduce the optimal center-of-mass displacements interpolated from CCSD(T)/CBS radial potential energy surfaces in both NBC7x and HBC6 test sets to within ±0.1 Å. We therefore conclude that each of these denisty functional methods, together with the aug-cc-pVDZ basis set, is suitable for producing equilibrium geometries of generic nonbonded complexes.

An NMR strategy to detect conformational differences in a protein complexed with highly analogous inhibitors in solution

This manuscript presents an NMR strategy to investigate conformational differences in protein-inhibitor complexes, when the inhibitors tightly bind to a protein at sub-nanomolar dissociation constants and are highly analogous to each other. Using HIV-1 protease (PR), we previously evaluated amide chemical shift differences, ΔCSPs, of PR bound to darunavir (DRV) compared to PR bound to several DRV analogue inhibitors, to investigate subtle but significant long-distance conformation changes caused by the inhibitor's chemical moiety variation [Khan, S. N., Persons, J. D. Paulsen, J. L., Guerrero, M., Schiffer, C. A., Kurt-Yilmaz, N., and Ishima, R., Biochemistry, (2018), 57, 1652-1662]. However, ΔCSPs are not ideal for investigating subtle PR-inhibitor interface differences because intrinsic differences in the electron shielding of the inhibitors affect protein ΔCSPs. NMR relaxation is also not suitable as it is not sensitive enough to detect small conformational differences in rigid regions among similar PR-inhibitor complexes. Thus, to gain insight into conformational differences at the inhibitor-protein interface, we recorded ¹⁵N-half filtered NOESY spectra of PR bound to two highly analogous inhibitors and assessed NOEs between PR amide protons and inhibitor protons, between PR amide protons and hydroxyl side chains, and between PR amide protons and water protons. We also verified the PR amide-water NOEs using 2D water-NOE/ROE experiments. Differences in water-amide proton NOE peaks, possibly due to amide-protein hydrogen bonds, were observed between subunit A and subunit B, and between the DRV-bound form and an analogous inhibitor-bound form, which may contribute to remote conformational changes.

Stacking Interactions of Heterocyclic Drug Fragments with Protein Amide Backbones

Stacking interactions can be important enthalpic contributors to drug binding. Among the less well-studied stacking interactions are those occurring between an arene and the π-face of an amide group. Given the ubiquity of heterocycles in drugs, combined with the abundance of amides in the protein backbone, optimizing these noncovalent interactions can provide a potential route to enhanced drug binding. Previously, Diederich et al. (ChemMedChem 2013, 8, 397-404) studied stacked dimers of a model amide with a set of 18 heterocycles, showing that computed interaction energies correlate with the dipole moments of the heterocycles and providing guidelines for the optimization of these interactions. We considered stacked dimers of the same model amide with a larger set of 28 heterocycles common in pharmaceuticals, by using more robust ab initio methods. While the overall trends in these new data corroborate many of the results of Diederich et al., these data provide a more refined view of the nature of amide stacking interactions. We present a robust scoring function for amide stacking interaction energies based on the molecular dipole moment and strength of the electric field above the arene.