Factor Analysis of Conformations and NMR Signals of Rotaxanes: AIMD and Polarizable MD Simulations

Ligand 1 H NMR Chemical Shifts as Accurate Reporters for Protein-Ligand Binding Interfaces in Solution

The availability of high-resolution 3D structural information is crucial for investigating guest-host systems across a wide range of fields. In the context of drug discovery, the information is routinely used to establish and validate structure-activity relationships, grow initial hits from screening campaigns, and to guide molecular docking. For the generation of protein-ligand complex structural information, X-ray crystallography is the experimental method of choice, however, with limited information on protein flexibility. An experimentally verified structural model of the binding interface in the native solution-state would support medicinal chemists in their molecular design decisions. Here we demonstrate that protein-bound ligand 1 H NMR chemical shifts are highly sensitive and accurate probes for the immediate chemical environment of protein-ligand interfaces. By comparing the experimental ligand 1 H chemical shift values with those computed from the X-ray structure using quantum mechanics methodology, we identify significant disagreements for parts of the ligand between the two experimental techniques. We show that quantum mechanics/molecular mechanics (QM/MM) molecular dynamics (MD) ensembles can be used to refine initial X-ray co-crystal structures resulting in a better agreement with experimental 1 H ligand chemical shift values. Overall, our findings highlight the usefulness of ligand 1 H NMR chemical shift information in combination with a QM/MM MD workflow for generating protein-ligand ensembles that accurately reproduce solution structural data.

Treating Polarization Effects in Charged and Polar Bio-Molecules Through Variable Electrostatic Parameters

Polarization plays important roles in charged and hydrogen bonding containing systems. Much effort ranging from the construction of physics-based models to quantum mechanism (QM)-based and machine learning (ML)-assisted models have been devoted to incorporating the polarization effect into the conventional force fields at different levels, such as atomic and coarse grained (CG). The application of polarizable force fields or polarization models was limited by two aspects, namely, computational cost and transferability. Different from physics-based models, no predetermining parameters were required in the QM-based approaches. Taking advantage of both the accuracy of QM calculations and efficiency of molecular mechanism (MM) and ML, polarization effects could be treated more efficiently while maintaining the QM accuracy. The computational cost could be reduced with variable electrostatic parameters, such as the charge, dipole, and electronic dielectric constant with the help of linear scaling fragmentation-based QM calculations and ML models. Polarization and entropy effects on the prediction of partition coefficient of druglike molecules are demonstrated by using both explicit or implicit all-atom molecular dynamics simulations and machine learning-assisted models. Directions and challenges for future development are also envisioned.

Pseudorotaxane formation affected by stereo-electronic effects. A theoretical and experimental study

We report a theoretical and experimental study on different complexes of pseudorotaxanes possessing pyridine axles. In order to evaluate the stereo-electronic effects of the methyl substituents in the pyridine ring, complexes with different substitution patterns were synthesized. In this way, it was possible to analyze the different behaviors of these complexes according to the positions of their methyl substituents. Combined techniques of molecular dynamics and quantum mechanical calculations with the help of molecular electrostatic potentials for a simpler visualization of the electronic effects were employed. We have sought experimental support of NMR spectroscopy analysis to corroborate the conclusions obtained from the molecular simulations. Our results not only clearly demonstrate that both electronic and steric effects play key roles in the feasibility of the formation of such complexes, but also the simulations reported here might predict the degree of difficulty of their formation. The combination of computational techniques employed here seems to be an excellent approach to be able to predict whether or not a complex can be formed and with what degree of difficulty. In addition, our experimental and theoretical results have allowed us to visualize the formation of external complexes in the rotaxanes reported here. In this case, the use of bolaforms with trimethylammonium groups at both ends was very useful to evaluate in detail the formation of the so-called external complexes in these systems.

Computational and data driven molecular material design assisted by low scaling quantum mechanics calculations and machine learning

Electronic structure methods based on quantum mechanics (QM) are widely employed in the computational predictions of the molecular properties and optoelectronic properties of molecular materials. The computational costs of these QM methods, ranging from density functional theory (DFT) or time-dependent DFT (TDDFT) to wave-function theory (WFT), usually increase sharply with the system size, causing the curse of dimensionality and hindering the QM calculations for large sized systems such as long polymer oligomers and complex molecular aggregates. In such cases, in recent years low scaling QM methods and machine learning (ML) techniques have been adopted to reduce the computational costs and thus assist computational and data driven molecular material design. In this review, we illustrated low scaling ground-state and excited-state QM approaches and their applications to long oligomers, self-assembled supramolecular complexes, stimuli-responsive materials, mechanically interlocked molecules, and excited state processes in molecular aggregates. Variable electrostatic parameters were also introduced in the modified force fields with the polarization model. On the basis of QM computational or experimental datasets, several ML algorithms, including explainable models, deep learning, and on-line learning methods, have been employed to predict the molecular energies, forces, electronic structure properties, and optical or electrical properties of materials. It can be conceived that low scaling algorithms with periodic boundary conditions are expected to be further applicable to functional materials, perhaps in combination with machine learning to fast predict the lattice energy, crystal structures, and spectroscopic properties of periodic functional materials.

Graph-Theory-Based Molecular Fragmentation for Efficient and Accurate Potential Surface Calculations in Multiple Dimensions

We present a multitopology molecular fragmentation approach, based on graph theory, to calculate multidimensional potential energy surfaces in agreement with post-Hartree-Fock levels of theory but at the density functional theory cost. A molecular assembly is coarse-grained into a set of graph-theoretic nodes that are then connected with edges to represent a collection of locally interacting subsystems up to an arbitrary order. Each of the subsystems is treated at two levels of electronic structure theory, the result being used to construct many-body expansions that are embedded within an ONIOM scheme. These expansions converge rapidly with the many-body order (or graphical rank) of subsystems and capture many-body interactions accurately and efficiently. However, multiple graphs, and hence multiple fragmentation topologies, may be defined in molecular configuration space that may arise during conformational sampling or from reactive, bond breaking and bond formation, events. Obtaining the resultant potential surfaces is an exponential scaling proposition, given the number of electronic structure computations needed. We utilize a family of graph-theoretic representations within a variational scheme to obtain multidimensional potential surfaces at a reduced cost. The fast convergence of the graph-theoretic expansion with increasing order of many-body interactions alleviates the exponential scaling cost for computing potential surfaces, with the need to only use molecular fragments that contain a fewer number of quantum nuclear degrees of freedom compared to the full system. This is because the dimensionality of the conformational space sampled by the fragment subsystems is much smaller than the full molecular configurational space. Additionally, we also introduce a multidimensional clustering algorithm, based on physically defined criteria, to reduce the number of energy calculations by orders of magnitude. The molecular systems benchmarked include coupled proton motion in protonated water wires. The potential energy surfaces and multidimensional nuclear eigenstates obtained are shown to be in very good agreement with those from explicit post-Hartree-Fock calculations that become prohibitive as the number of quantum nuclear dimensions grows. The developments here provide a rigorous and efficient alternative to this important chemical physics problem.

Controlled Fluorescence Enhancement of DNA-Binding Dye Through Chain Length Match between Oligoguanine and TOTO

Fluorescent DNA-binding dyes are extensively employed as probe and biosensing in biological detection and imaging. Experiments and theoretical calculations of thiazole orange homodimeric (TOTO) dye binding to a single-strand DNA (ssDNA), poly(dG)_n (n = 2, 4, 6, 8), reveal that the n = 6 complex shows about 300-fold stronger fluorescence than n = 2, 4 and a slightly stronger one than n = 8 complexes, which is benefited from the length match between TOTO and poly(dG)₆. The machine learning, based on molecular dynamics trajectories, indicates that TOTO is featured by the dihedral angle along its backbone and its end-to-end distance, in which the latter one defines the stretch and hairpin structures of TOTO, respectively. The time-dependent density functional theory calculations on the low-lying excited states show that the stretched TOTO with π-π end-stacking binding mode can bring about strong fluorescence with localized π-π^* transitions. For the n = 2, 4, and 8 complexes, the linear scaling quantum mechanics calculations indicate that the dominant hairpin TOTO with intercalative binding modes have relatively larger binding energies, leading to fluorescence quenching by intramolecular charge transfer. Our results may provide an insight for modulating the DNA-dye binding modes to tune the degree of charge transfer and designing fluorescent probes for the recognition of specific DNA sequences.

Structures and properties of large supramolecular coordination complexes predicted with the generalized energy-based fragmentation method

The generalized energy-based fragmentation (GEBF) method has been extended to facilitate ab initio calculations of large supramolecular coordination complexes.

Design of [2]rotaxane through image threshold segmentation of electrostatic potential image

An electrostatic potential (ESP)-based image segmentation method has been used to estimate the ability of proton donation and acceptance involved in ring-rod recognition. The relative binding strength of [2]rotaxane has also been further estimated from the difference of the characteristic image-segmentation derived ESP between proton donor and proton acceptor. The size and electrostatic compatibility criteria are introduced to guide the design of interlocked [2]rotaxane. A library of 75 thermodynamically stable [2]rotaxane candidates has been generated, including 16 experimentally known systems. The theoretical results for 16 experimentally known [2]rotaxanes are in good agreement with both the experimental association constants and density functional theory-calculated binding energies. Our ESP-based image segmentation model is also applicable to the tristable [2]rotaxane molecular shuttle as well as [1]rotaxane with self-inclusion function, indicating this simple method is generic in the field of constructing other supramolecular architectures formed with donor/acceptor molecular recognition. © 2016 Wiley Periodicals, Inc.