Semi-empirical molecular orbital methods including dispersion corrections for the accurate prediction of the full range of intermolecular interactions in biomolecules

Quantum Mechanics-Based Fast and Reliable Prediction of Binding Pose Structures

Predicting the binding poses of docking with an accurate estimation of binding energies is highly important but very challenging in computational drug design. A quantum mechanics (QM) calculation-based docking approach considering multiple conformations and orientations of the ligand is introduced here to tackle the problem. This QM docking consists of three steps: generating an ensemble of binding poses with a conventional docking simulation, computing the binding energies with self-consistent charge density functional theory tightly binding with dispersion correction (DFTB-D) to selecting the 10 top binding modes, and optimizing the selected binding mode structures using the ONIOM(DFTB:PM7) technique to determine the binding poses. The ONIOM(DFTB-D:PM6) docking approach is tested on 121 ligand-receptor biocomplexes with the crystal structures obtained from the Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB). The result shows that the new method is highly satisfactory for the accurate prediction of the binding poses. The new docking method should be beneficial to structure-based drug design.

Modeling the Antiviral Activity of Ginkgo biloba Polyphenols against Variola: In Silico Exploration of Inhibitory Candidates for VarTMPK and HssTMPK Enzymes

BACKGROUND

The aim of this study is to use modeling methods to estimate the antiviral activity of natural molecules extracted from Ginkgo biloba for the treatment of variola which is a zoonotic disease posing a growing threat to human survival. The recent spread of variola in nonendemic countries and the possibility of its use as a bioterrorism weapon have made it a global threat once again. Therefore, the search for new antiviral therapies with reduced side effects is necessary.

METHODS

In this study, we examined the interactions between polyphenolic compounds from Ginkgo biloba, a plant known for its antiviral activity, and two enzymes involved in variola treatment, VarTMPK and HssTMPK, using molecular docking.

RESULTS

The obtained docking scores showed that among the 152 selected polyphenolic compounds; many ligands had high inhibitory potential according to the energy affinity. By considering Lipinski's rules, we found that Liquiritin and Olivil molecules are the best candidates to be developed into drugs that inhibit VarTMPK because of their high obtained scores compared to reference ligands, and zero violations of Lipinski's rules. We also found that ginkgolic acids have good affinities with HssTMPK and acceptable physicochemical properties to be developed into drugs administered orally.

CONCLUSION

Based on the obtained scores and Lipinski's rules, Liquiritin, Olivil, and ginkgolic acids molecules showed interesting results for both studied enzymes, indicating the existence of promising and moderate activity of these polyphenols for the treatment of variola and for possible multi-targeting. Liquiritin has been shown to exhibit anti-inflammatory effects on various inflammation- related diseases such as skin injury, hepatic inflammatory injury, and rheumatoid arthritis. Olivil has been shown to have antioxidant activity. Olivil derivatives have also been studied for their potential use as anticancer agents. Ginkgolic acids have been shown to have antimicrobial and antifungal properties. However, ginkgolic acids are also known to cause allergic reactions in some people. Therefore, future studies should consider these results and explore the potential of these compounds as antiviral agents. Further experimental studies in-vitro and in-vivo are required to validate and scale up these findings.

Exploring the Therapeutic Potential of Ginkgo biloba Polyphenols in Targeting Biomarkers of Colorectal Cancer: An In-silico Evaluation

BACKGROUND

Colorectal cancer (CRC) is a major contributor to cancer-related deaths worldwide, driving the need for effective anticancer therapies with fewer side effects. The exploration of Ginkgo biloba, a natural source, offers a hopeful avenue for novel treatments targeting key colorectal biomarkers involved in CRC treatment.

OBJECTIVE

The aim of this study was to explore the binding affinity of natural molecules derived from G. biloba to essential biomarkers associated with CRC, including Kirsten rat sarcoma virus, neuroblastoma RAS mutations, serine/threonine-protein kinase B-Raf, phosphatidylinositol 3'-kinase, and deleted colorectal cancer, using molecular docking. The focus of this research was to evaluate how effectively these molecules bind to specified targets in order to identify potential inhibitors for the treatment of CRC.

METHODS

A total of 152 polyphenolic compounds from G. biloba were selected and subjected to molecular docking simulations to evaluate their interactions with CRC-related biomarkers. The docking results were analysed to identify ligands exhibiting strong affinities towards the targeted genes, suggesting potential inhibitory effects.

RESULTS

Docking simulations unveiled the strong binding affinities between selected polyphenolic compounds derived from G. biloba and genes associated with CRC. The complex glycoside structures that are found in flavonols are of significant importance. These compounds, including derivatives with distinctive arrangements, exhibited promising docking scores, signifying substantial interactions with the targeted biomarkers.

CONCLUSION

The study demonstrates the potential of G. biloba-derived molecules as effective anticancer agents for colorectal cancer. The identified ligands exhibit strong interactions with crucial CRC-related biomarkers, suggesting potential inhibition ability. Further in vitro and in vivo investigations are needed to validate and build upon these promising findings, advancing the development of novel and efficient CRC therapies.

Minimal Basis Set Hartree-Fock Corrected with Atom-Centered Potentials for Molecular Crystal Modeling and Crystal Structure Prediction

Crystal structure prediction (CSP), determining the experimentally observable structure of a molecular crystal from the molecular diagram, is an important challenge with technologically relevant applications in materials manufacturing and drug design. For the purpose of screening the randomly generated candidate crystal structures, CSP protocols require energy ranking methods that are fast and can accurately capture the small energy differences between molecular crystals. In addition, a good ranking method should also produce accurate equilibrium geometries, both intramolecular and intermolecular. In this article, we explore the combination of minimal-basis-set Hartree-Fock (HF) with atom-centered potentials (ACPs) as a method for modeling the structure and energetics of molecular crystals. The ACPs are developed for the H, C, N, and O atoms and fitted to a set of reference data at the B86bPBE-XDM level in order to mitigate basis-set incompleteness and missing correlation. In particular, ACPs are developed in combination with two methods: HF-D3/MINIs and HF-3c. The application of ACPs greatly improves the performance of HF-D3/MINIs for lattice energies, crystal energy differences, energy-volume and energy-strain relations, and crystal geometries. In the case of HF-3c, the improvement in the crystal energy differences is much smaller than in HF-D3/MINIs, but lattice energies and particularly crystal geometries are considerably better when ACPs are used. The resulting methods may be useful for CSP but also for quick calculation of molecular crystal lattice energies and geometries.

New Approach for Correcting Noncovalent Interactions in Semiempirical Quantum Mechanical Methods: The Importance of Multiple-Orientation Sampling

A new approach is presented to improve the performance of semiempirical quantum mechanical (SQM) methods in the description of noncovalent interactions. To show the strategy, the PM6 Hamiltonian was selected, although, in general, the procedure can be applied to other semiempirical Hamiltonians and to different methodologies. A set of small molecules were selected as representative of various functional groups, and intermolecular potential energy curves (IPECs) were evaluated for the most relevant orientations of interacting molecular pairs. Then, analytical corrections to PM6 were derived from fits to B3LYP-D3/def2-TZVP reference–PM6 interaction energy differences. IPECs provided by the B3LYP-D3/def2-TZVP combination of the electronic structure method and basis set were chosen as the reference because they are in excellent agreement with CCSD(T)/aug-cc-pVTZ curves for the studied systems. The resulting method, called PM6-FGC (from functional group corrections), significantly improves the performance of PM6 and shows the importance of including a sufficient number of orientations of the interacting molecules in the reference data set in order to obtain well-balanced descriptions.

Quantum chemical benchmark databases of gold-standard dimer interaction energies

Advances in computational chemistry create an ongoing need for larger and higher-quality datasets that characterize noncovalent molecular interactions. We present three benchmark collections of quantum mechanical data, covering approximately 3,700 distinct types of interacting molecule pairs. The first collection, which we refer to as DES370K, contains interaction energies for more than 370,000 dimer geometries. These were computed using the coupled-cluster method with single, double, and perturbative triple excitations [CCSD(T)], which is widely regarded as the gold-standard method in electronic structure theory. Our second benchmark collection, a core representative subset of DES370K called DES15K, is intended for more computationally demanding applications of the data. Finally, DES5M, our third collection, comprises interaction energies for nearly 5,000,000 dimer geometries; these were calculated using SNS-MP2, a machine learning approach that provides results with accuracy comparable to that of our coupled-cluster training data. These datasets may prove useful in the development of density functionals, empirically corrected wavefunction-based approaches, semi-empirical methods, force fields, and models trained using machine learning methods.

Adventures in DFTB: Toward an Automatic Parameterization Scheme

As we push forward on understanding the fate of chemicals in the environment, we need a method that will allow for the simulation of the inherent heterogeneity. Density functional tight binding (DFTB) is a methodology that allows for a detailed electronic description and would be ideal for this problem. While many parameters can be derived directly from DFT, empirical parameters still exist in the confinement and repulsion potentials. In this manuscript, we examine these potentials and present solutions that will minimize the degree of empiricism. Our results show that it is possible to construct confinement potentials from examining the atomic radial wavefunctions. Moreover, we found that the heterogeneous repulsion potentials can be derived from using only homogeneous repulsion curves.

Reactivity prediction in aza-Michael additions without transition state calculations: the Ames test for mutagenicity

Animal testing remains a contentious ethical issue in predictive toxicology. Thus, a fast, versatile, low-cost quantum chemical model is presented for predicting the risk of Ames mutagenicity in a series of 1,4 Michael acceptor type compounds. This framework eliminates the need for transition state calculations, and uses an intermediate structure to probe the reactivity of aza-Michael acceptors. This model can be used in a variety of settings e.g., the design of targeted covalent inhibitors and polyketide biosyntheses.

SQM/COSMO Scoring Function: Reliable Quantum-Mechanical Tool for Sampling and Ranking in Structure-Based Drug Design

Quantum mechanical (QM) methods have been gaining importance in structure-based drug design where a reliable description of protein-ligand interactions is of utmost significance. However, strategies i. e. QM/MM, fragmentation or semiempirical (SQM) methods had to be pursued to overcome the unfavorable scaling of QM methods. Various SQM-based approaches have significantly contributed to the accuracy of docking and improvement of lead compounds. Parametrizations of SQM and implicit solvent methods in our laboratory have been instrumental to obtain a reliable SQM-based scoring function. The experience gained in its application for activity ranking of ligands binding to tens of protein targets resulted in setting up a faster SQM/COSMO scoring approach, which outperforms standard scoring methods in native pose identification for two dozen protein targets with ten thousand poses. Recently, SQM/COSMO was effectively applied in a proof-of-concept study of enrichment in virtual screening. Due to its superior performance, feasibility and chemical generality, we propose the SQM/COSMO approach as an efficient tool in structure-based drug design.

What Next for Quantum Mechanics in Structure-Based Drug Discovery?

There is significant potential for electronic structure methods to improve the quality of the predictions furnished by the tools of computer-aided drug design, which typically rely on empirically derived functions. In this perspective, we consider some recent examples of how quantum mechanics has been applied in predicting protein-ligand geometries, protein-ligand binding affinities and ligand strain on binding. We then outline several significant developments in quantum mechanics methodology likely to influence these approaches: in particular, we note the advent of more computationally expedient ab initio quantum mechanical methods that can provide chemical accuracy for larger molecular systems than hitherto possible. We highlight the emergence of increasingly accurate semiempirical quantum mechanical methods and the associated role of machine learning and molecular databases in their development. Indeed, the convergence of improved algorithms for solving and analyzing electronic structure, modern machine learning methods, and increasingly comprehensive benchmark data sets of molecular geometries and energies provides a context in which the potential of quantum mechanics will be increasingly realized in driving future developments and applications in structure-based drug discovery.

Fragmentation Spectra Prediction and DNA Adducts Structural Determination

In this work, chemical dynamics simulations were optimized and used to predict fragmentation mass spectra for DNA adduct structural determination. O⁶-methylguanine (O⁶-Me-G) was used as a simple model adduct to calculate theoretical spectra for comparison with measured high-resolution fragmentation data. An automatic protocol was established to consider the different tautomers accessible at a given energy and obtain final theoretical spectra by insertion of an initial tautomer. In the work reported here, the most stable tautomer was chosen as the initial structure, but in general, any structure could be considered. Allowing for the formation of the various possible tautomers during simulation calculations was found to be important to getting a more complete fragmentation spectrum. The calculated theoretical results reproduce the experimental peaks such that it was possible to determine reaction pathways and product structures. The calculated tautomerization network was crucial to correctly identifying all the observed ion peaks, showing that a mobile proton model holds not only for peptide fragmentation but also for nucleobases. Finally, first principles results were compared to simple machine learning fragmentation models.

Semiempirical Quantum-Chemical Methods with Orthogonalization and Dispersion Corrections

We present two new semiempirical quantum-chemical methods with orthogonalization and dispersion corrections: ODM2 and ODM3 (ODM x). They employ the same electronic structure model as the OM2 and OM3 (OM x) methods, respectively. In addition, they include Grimme's dispersion correction D3 with Becke-Johnson damping and three-body corrections E ABC for Axilrod-Teller-Muto dispersion interactions as integral parts. Heats of formation are determined by adding explicitly computed zero-point vibrational energy and thermal corrections, in contrast to standard MNDO-type and OM x methods. We report ODM x parameters for hydrogen, carbon, nitrogen, oxygen, and fluorine that are optimized with regard to a wide range of carefully chosen state-of-the-art reference data. Extensive benchmarks show that the ODM x methods generally perform better than the available MNDO-type and OM x methods for ground-state and excited-state properties, while they describe noncovalent interactions with similar accuracy as OM x methods with a posteriori dispersion corrections.

Design and Synthesis of Non-Peptide Mimetics Mapping the Immunodominant Myelin Basic Protein (MBP83-96) Epitope to Function as T-Cell Receptor Antagonists

Encephalitogenic T cells are heavily implicated in the pathogenesis of multiple sclerosis (MS), an autoimmune demyelinating disease of the central nervous system. Their stimulation is triggered by the formation of a trimolecular complex between the human leukocyte antigen (HLA), an immunodominant myelin basic protein (MBP) epitope, and the T cell receptor (TCR). We detail herein our studies directed towards the rational design and synthesis of non-peptide mimetic molecules, based on the immunodominant MBP_83-96 epitope that is recognized by the TCR in complex with HLA. We focused our attention on the inhibition of the trimolecular complex formation and consequently the inhibition of proliferation of activated T cells. A structure-based pharmacophore model was generated, in view of the interactions between the TCR and the HLA-MBP_83-96 complex. As a result, new candidate molecules were designed based on lead compounds obtained through the ZINC database. Moreover, semi-empirical and density functional theory methods were applied for the prediction of the binding energy between the proposed non-peptide mimetics and the TCR. We synthesized six molecules that were further evaluated in vitro as TCR antagonists. Analogues 15 and 16 were able to inhibit to some extent the stimulation of T cells by the immunodominant MBP_83-99 peptide from immunized mice. Inhibition was followed to a lesser degree by analogues 17 and 18 and then by analogue 19. These studies show that lead compounds 15 and 16 may be used for immunotherapy against MS.

Semiempirical Quantum Mechanical Methods for Noncovalent Interactions for Chemical and Biochemical Applications

Semiempirical (SE) methods can be derived from either Hartree-Fock or density functional theory by applying systematic approximations, leading to efficient computational schemes that are several orders of magnitude faster than ab initio calculations. Such numerical efficiency, in combination with modern computational facilities and linear scaling algorithms, allows application of SE methods to very large molecular systems with extensive conformational sampling. To reliably model the structure, dynamics, and reactivity of biological and other soft matter systems, however, good accuracy for the description of noncovalent interactions is required. In this review, we analyze popular SE approaches in terms of their ability to model noncovalent interactions, especially in the context of describing biomolecules, water solution, and organic materials. We discuss the most significant errors and proposed correction schemes, and we review their performance using standard test sets of molecular systems for quantum chemical methods and several recent applications. The general goal is to highlight both the value and limitations of SE methods and stimulate further developments that allow them to effectively complement ab initio methods in the analysis of complex molecular systems.

Semiempirical Quantum-Chemical Orthogonalization-Corrected Methods: Theory, Implementation, and Parameters

Semiempirical orthogonalization-corrected methods (OM1, OM2, and OM3) go beyond the standard MNDO model by explicitly including additional interactions into the Fock matrix in an approximate manner (Pauli repulsion, penetration effects, and core–valence interactions), which yields systematic improvements both for ground-state and excited-state properties. In this Article, we describe the underlying theoretical formalism of the OMx methods and their implementation in full detail, and we report all relevant OMx parameters for hydrogen, carbon, nitrogen, oxygen, and fluorine. For a standard set of mostly organic molecules commonly used in semiempirical method development, the OMx results are found to be superior to those from standard MNDO-type methods. Parametrized Grimme-type dispersion corrections can be added to OM2 and OM3 energies to provide a realistic treatment of noncovalent interaction energies, as demonstrated for the complexes in the S22 and S66×8 test sets.

Modelling of graphene functionalization

This perspective describes the available theoretical methods and models for simulating graphene functionalization based on quantum and classical mechanics.

Development of Semiempirical Models for Proton Transfer Reactions in Water

This letter presents a method for the parametrization of semiempirical models for proton transfer reactions in water clusters. Two new models are developed: AM1-W, which is a reparameterization of the classic AM1 model, and AM1PG-W, which is a modified AM1-like model including a pairwise correction to the core repulsion function. Both models show good performance on hydrogen-bonding energies and on proton transfer energy profiles, which are of great importance for proton transfer reactions in large water clusters and in proteins. The parametrization method introduced is general and can be used to develop any other system-specific semiempirical models.

Performance of B3LYP Density Functional Methods for a Large Set of Organic Molecules

Testing of the commonly used hybrid density functional B3LYP with the 6-31G(d), 6-31G(d,p), and 6-31+G(d,p) basis sets has been carried out for 622 neutral, closed-shell organic compounds containing the elements C, H, N, and O. The focus is comparison of computed and experimental heats of formation and isomerization energies. In addition, the effect of an empirical dispersion correction term has been evaluated and found to improve agreement with the experimental data. For the 622 compounds, the mean absolute errors (MAE) in the heats of formation are 3.1, 2.6, 2.7, and 2.4 kcal/mol for B3LYP/6-31G(d), B3LYP/6-31G(d,p), B3LYP/6-31+G(d,p), and B3LYP/6-31+G(d,p) with the dispersion correction. A diverse set of 34 isomerizations highlights specific issues of general interest, such as performance on differences in steric effects, conjugation, and bonding. The corresponding MAEs for the isomerizations are 2.7, 2.4, 2.2, and 1.9 kcal/mol. Improvement is obtained for isomerizations of amines and alcohols when both polarization and diffuse functions are used, but the overstabilization of linear alkanes compared to branched isomers can be relieved only with the dispersion correction. Besides the insights on DFT methods, the study also aimed to quantify the gains in accuracy that can be achieved by replacing energetics from NDO-based semiempirical methods with DFT results. Since the MAEs obtained with the PDDG/PM3 method for the 622 heats of formation and 34 isomerizations are 2.8 and 2.3 kcal/mol, negligible advantage in accuracy for the B3LYP-based methods emerged in the absence of the dispersion corrections.

Improved Hydrogen Bonding at the NDDO-Type Semiempirical Quantum Mechanical/Molecular Mechanical Interface

A semiempirical quantum mechanical (QM)/molecular mechanical (MM) potential with reformulated QM core-MM charge interactions is introduced, specifically to more accurately model hydrogen bonding at the QM/MM interface. Application of this potential using the PM3 Hamiltonian shows improved prediction of geometry and interaction energy for hydrogen bonded small molecule complexes typical of biomolecular interactions, without significantly impacting the modeling of other interaction types. Using this potential, more quantitative prediction of interaction energies is also found at a protein-ligand interface.

The nonadditivity of stacking interactions in adenine–thymine and guanine–cytosine stacked structures: Study by MP2 and SCS-MP2 calculations

The nonadditivity of stacking interactions in stacked structures of adenine–thymine and guanine–cytosine base pairs is investigated by MP2 and SCS-MP2 calculations with 6-311++G** and aug-cc-pvdz basis sets. The calculation results indicate that the intermolecular distances in the multi-stacked structures do not become shorter obviously as the stacked structure added. The middle stacking interaction energies in the multi-stacked structures also become weaker than that of dimer structures. It is found that the total stacking interaction energies of the trimer and tetramer stacked structures do not increase proportionally. Based on the results, we suggest that there is negative cooperativity of the stacking interactions in the adenine–thymine and guanine–cytosine stacked structures.

Nucleic acid reactivity: challenges for next-generation semiempirical quantum models

Semiempirical quantum models are routinely used to study mechanisms of RNA catalysis and phosphoryl transfer reactions using combined quantum mechanical (QM)/molecular mechanical methods. Herein, we provide a broad assessment of the performance of existing semiempirical quantum models to describe nucleic acid structure and reactivity to quantify their limitations and guide the development of next-generation quantum models with improved accuracy. Neglect of diatomic differential overlap and self-consistent density-functional tight-binding semiempirical models are evaluated against high-level QM benchmark calculations for seven biologically important datasets. The datasets include: proton affinities, polarizabilities, nucleobase dimer interactions, dimethyl phosphate anion, nucleoside sugar and glycosidic torsion conformations, and RNA phosphoryl transfer model reactions. As an additional baseline, comparisons are made with several commonly used density-functional models, including M062X and B3LYP (in some cases with dispersion corrections). The results show that, among the semiempirical models examined, the AM1/d-PhoT model is the most robust at predicting proton affinities. AM1/d-PhoT and DFTB3-3ob/OPhyd reproduce the MP2 potential energy surfaces of 6 associative RNA phosphoryl transfer model reactions reasonably well. Further, a recently developed linear-scaling "modified divide-and-conquer" model exhibits the most accurate results for binding energies of both hydrogen bonded and stacked nucleobase dimers. The semiempirical models considered here are shown to underestimate the isotropic polarizabilities of neutral molecules by approximately 30%. The semiempirical models also fail to adequately describe torsion profiles for the dimethyl phosphate anion, the nucleoside sugar ring puckers, and the rotations about the nucleoside glycosidic bond. The modeling of pentavalent phosphorus, particularly with thio substitutions often used experimentally as mechanistic probes, was problematic for all of the models considered. Analysis of the strengths and weakness of the models suggests that the creation of robust next-generation models should emphasize the improvement of relative conformational energies and barriers, and nonbonded interactions.

Water interactions with hydrophobic groups: assessment and recalibration of semiempirical molecular orbital methods

In this work, we present a study of the ability of different semiempirical methods to describe intermolecular interactions in water solution. In particular, we focus on methods based on the Neglect of Diatomic Differential Overlap approximation. Significant improvements of these methods have been reported in the literature in the past years regarding the description of non-covalent interactions. In particular, a broad range of methodologies has been developed to deal with the properties of hydrogen-bonded systems, with varying degrees of success. In contrast, the interactions between water and a molecule containing hydrophobic groups have been little analyzed. Indeed, by considering the potential energy surfaces obtained using different semiempirical Hamiltonians for the intermolecular interactions of model systems, we found that none of the available methods provides an entirely satisfactory description of both hydrophobic and hydrophilic interactions in water. In addition, a vibrational analysis carried out in a model system for these interactions, a methane clathrate cluster, showed that some recent methods cannot be used to carry out studies of vibrational properties. Following a procedure established in our group [M. I. Bernal-Uruchurtu, M. T. C. Martins-Costa, C. Millot, and M. F. Ruiz-López, J. Comput. Chem. 21, 572 (2000); W. Harb, M. I. Bernal-Uruchurtu, and M. F. Ruiz-López, Theor. Chem. Acc. 112, 204 (2004)], we developed new parameters for the core-core interaction terms based on fitting potential energy curves obtained at the MP2 level for our model system. We investigated the transferability of the new parameters to describe a system, having both hydrophilic and hydrophobic groups, interacting with water. We found that only by introducing two different sets of parameters for hydrophilic and hydrophobic hydrogen atom types we are able to match the features of the ab initio calculated properties. Once this assumption is made, a good agreement with the MP2 reference is achieved. The results reported in this work provide therefore a direction for future developments of semiempirical approaches that are still required to investigate chemical processes in biomolecules and in large disordered systems.

Enhanced semiempirical QM methods for biomolecular interactions

Recent successes and failures of the application of 'enhanced' semiempirical QM (SQM) methods are reviewed in the light of the benefits and backdraws of adding dispersion (D) and hydrogen-bond (H) correction terms. We find that the accuracy of SQM-DH methods for non-covalent interactions is very often reported to be comparable to dispersion-corrected density functional theory (DFT-D), while computation times are about three orders of magnitude lower. SQM-DH methods thus open up a possibility to simulate realistically large model systems for problems both in life and materials science with comparably high accuracy.

Structure and optoelectronic properties of helical pyridine–furan, pyridine–pyrrole and pyridine–thiophene oligomers

The optoelectronic properties of helical structures pyridine–furan, pyridine–pyrrole and pyridine–thiophene oligomers are explored using density functional theory methods.

The relative roles of electrostatics and dispersion in the stabilization of halogen bonds

In this work we highlight recent work aimed at the characterization of halogen bonds. Here we discuss the origins of the σ-hole, the modulation of halogen bond strength by changing of neighboring chemical groups (i.e. halogen bond tuning), the performance of various computational methods in treating halogen bonds, and the strength and character of the halogen bond, the dihalogen bond, and two hydrogen bonds in bromomethanol dimers (which serve as model complexes) are compared. Symmetry adapted perturbation theory analysis of halogen bonding complexes indicates that halogen bonds strongly depend on both dispersion and electrostatics. The electrostatic interaction that occurs between the halogen σ-hole and the electronegative halogen bond donor is responsible for the high degree of directionality exhibited by halogen bonds. Because these noncovalent interactions have a strong dispersion component, it is important that the computational method used to treat a halogen bonding system be chosen very carefully, with correlated methods (such as CCSD(T)) being optimal. It is also noted here that most forcefield-based molecular mechanics methods do not describe the halogen σ-hole, and thus are not suitable for treating systems with halogen bonds. Recent attempts to improve the molecular mechanics description of halogen bonds are also discussed.

Parametrization of an Orbital-Based Linear-Scaling Quantum Force Field for Noncovalent Interactions

We parametrize a linear-scaling quantum mechanical force field called mDC for the accurate reproduction of nonbonded interactions. We provide a new benchmark database of accurate ab initio interactions between sulfur-containing molecules. A variety of nonbond databases are used to compare the new mDC method with other semiempirical, molecular mechanical, ab initio, and combined semiempirical quantum mechanical/molecular mechanical methods. It is shown that the molecular mechanical force field significantly and consistently reproduces the benchmark results with greater accuracy than the semiempirical models and our mDC model produces errors twice as small as the molecular mechanical force field. The comparisons between the methods are extended to the docking of drug candidates to the Cyclin-Dependent Kinase 2 protein receptor. We correlate the protein-ligand binding energies to their experimental inhibition constants and find that the mDC produces the best correlation. Condensed phase simulation of mDC water is performed and shown to produce O-O radial distribution functions similar to TIP4P-EW.

Computational approaches to predicting the impact of novel bases on RNA structure and stability

The use of computational modeling techniques to gain insight into nucleobase interactions has been a challenging endeavor to date. Accurate treatment requires the tackling of many challenges but also holds the promise of great rewards. The development of effective computational approaches to predict the binding affinities of nucleobases and analogues can, for example, streamline the process of developing novel nucleobase modifications, which should facilitate the development of new RNAi-based therapeutics. This brief review focuses on available computational approaches to predicting base pairing affinity in RNA-based contexts such as nucleobase-nucleobase interactions in duplexes and nucleobase-protein interactions. The challenges associated with such modeling along with potential future directions for the field are highlighted.

Quantum mechanical force field for water with explicit electronic polarization

A quantum mechanical force field (QMFF) for water is described. Unlike traditional approaches that use quantum mechanical results and experimental data to parameterize empirical potential energy functions, the present QMFF uses a quantum mechanical framework to represent intramolecular and intermolecular interactions in an entire condensed-phase system. In particular, the internal energy terms used in molecular mechanics are replaced by a quantum mechanical formalism that naturally includes electronic polarization due to intermolecular interactions and its effects on the force constants of the intramolecular force field. As a quantum mechanical force field, both intermolecular interactions and the Hamiltonian describing the individual molecular fragments can be parameterized to strive for accuracy and computational efficiency. In this work, we introduce a polarizable molecular orbital model Hamiltonian for water and for oxygen- and hydrogen-containing compounds, whereas the electrostatic potential responsible for intermolecular interactions in the liquid and in solution is modeled by a three-point charge representation that realistically reproduces the total molecular dipole moment and the local hybridization contributions. The present QMFF for water, which is called the XP3P (explicit polarization with three-point-charge potential) model, is suitable for modeling both gas-phase clusters and liquid water. The paper demonstrates the performance of the XP3P model for water and proton clusters and the properties of the pure liquid from about 900 × 10(6) self-consistent-field calculations on a periodic system consisting of 267 water molecules. The unusual dipole derivative behavior of water, which is incorrectly modeled in molecular mechanics, is naturally reproduced as a result of an electronic structural treatment of chemical bonding by XP3P. We anticipate that the XP3P model will be useful for studying proton transport in solution and solid phases as well as across biological ion channels through membranes.

In silico design: extended molecular dynamic simulations of a new series of dually acting inhibitors against EGFR and HER2

Based on the hit structures that have been identified in our previous studies against EGFR and HER2, new potential inhibitors that share the same scaffold of the hit structures are designed and screened in silico. Insights into understanding the potential inhibitory effect of the new inhibitors against both EGFR and HER2 receptors is obtained using extended molecular dynamics (MD) simulations and different scoring techniques. The binding mechanisms and dynamics are detailed with respect to two approved inhibitors against EGFR (lapatinib) and HER2 (SYR127063). The best scoring inhibitor (T9) is chosen for additional in silico investigation against both the wild-type and T790M mutant strain of EGFR and the wild-type HER2. The results reveal that certain substitution patterns increase the stability and assure stronger binding and higher H-bond occupancy of the conserved water molecule that is commonly observed with kinase crystal structures. Furthermore, the new inhibitor (T9) forms stable interactions with the mutant strain as a direct consequence of the enhanced ability to form additional hydrogen bonding interactions with binding site residues.

A Caveat on SCC-DFTB and Noncovalent Interactions Involving Sulfur Atoms

Accurate modeling of noncovalent interactions involving sulfur today is ubiquitous, particularly with regard to the role played by sulfur-containing heterocycles in the field of organic electronics. The density functional tight binding (DFTB) method offers a good compromise between computational efficiency and accuracy, enabling the treatment of thousands of atoms at a fraction of the cost of density functional theory (DFT) evaluations. DFTB is an approximate quantum chemical approach that is based on the DFT total energy expression. Here, we address a critical issue inherent to the DFTB parametrization, which prevents the use of the DFTB framework for simulating noncovalent interactions involving sulfur atoms and precludes its combination with a dispersion correction. (1-5) Dramatic examples of structural patterns relevant to the field of organic electronics illustrate that DFTB delivers erroneous (i.e., qualitatively wrong) results involving spurious binding.

Comparison of molecular mechanics, semi-empirical quantum mechanical, and density functional theory methods for scoring protein-ligand interactions

Correctly ranking protein-ligand interactions with respect to overall free energy of binding is a grand challenge for virtual drug design. Here we compare the performance of various quantum chemical approaches for tackling this so-called "scoring" problem. Relying on systematically generated benchmark sets of large protein/ligand model complexes based on the PDBbind database, we show that the performance depends first of all on the general level of theory. Comparing classical molecular mechanics (MM), semiempirical quantum mechanical (SQM), and density functional theory (DFT) based methods, we find that enhanced SQM approaches perform very similar to DFT methods and substantially different from MM potentials.