Alternative CNDOL Fockians for fast and accurate description of molecular exciton properties.

CNDOL is an a priori, approximate Fockian for molecular wave functions. In this study, we employ several modes of singly excited configuration interaction (CIS) to model molecular excitation properties by using four combinations of the one electron operator terms. Those options are compared to the experimental and theoretical data for a carefully selected set of molecules. The resulting excitons are represented by CIS wave functions that encompass all valence electrons in the system for each excited state energy. The Coulomb-exchange term associated to the calculated excitation energies is rationalized to evaluate theoretical exciton binding energies. This property is shown to be useful for discriminating the charge donation ability of molecular and supermolecular systems. Multielectronic 3D maps of exciton formal charges are showcased, demonstrating the applicability of these approximate wave functions for modeling properties of large molecules and clusters at nanoscales. This modeling proves useful in designing molecular photovoltaic devices. Our methodology holds potential applications in systematic evaluations of such systems and the development of fundamental artificial intelligence databases for predicting related properties.

Electronic Descriptors for Supervised Spectroscopic Predictions.

Spectroscopic properties of molecules hold great importance for the description of the molecular response under the effect of UV/vis electromagnetic radiation. Computationally expensive ab initio (e.g., MultiConfigurational SCF, Coupled Cluster) or TDDFT methods are commonly used by the quantum chemistry community to compute these properties. In this work, we propose a (supervised) Machine Learning approach to model the absorption spectra of organic molecules. Several supervised ML methods have been tested such as Kernel Ridge Regression (KRR), Multiperceptron Neural Networs (MLP), and Convolutional Neural Networks. [Ramakrishnan et al. J. Chem. Phys. 2015, 143, 084111. Ghosh et al. Adv. Sci. 2019, 6, 1801367.] The use of only geometrical-atomic number descriptors (e.g., Coulomb Matrix) proved to be insufficient for an accurate training. [Ramakrishnan et al. J. Chem. Phys. 2015, 143, 084111.] Inspired by the TDDFT theory, we propose to use a set of electronic descriptors obtained from low-cost DFT methods: orbital energy differences (Δϵia = ϵa - ϵi), transition dipole moment between occupied and unoccupied Kohn-Sham orbitals (⟨ϕi|r|ϕa⟩), and when relevant, charge-transfer character of monoexcitations (Ria). We demonstrate that with these electronic descriptors and the use of Neural Networks we can predict not only a density of excited states but also get a very good estimation of the absorption spectrum and charge-transfer character of the electronic excited states, reaching results close to chemical accuracy (∼2 kcal/mol or ∼0.1 eV).

1,3 Dipolar Cycloaddition of Münchnones: Factors behind the Regioselectivity.

The 1,3 dipolar cycloaddition reactions of münchnones and alkenes provide an expedite synthetic way to substituted pyrroles, an exceedingly important structural motif in the pharmaceutical and material science fields of research. The factors governing their regioselectivity rationalization are not well understood. Using several approaches, we investigate a set of 14 reactions (featuring two münchnones, 12 different alkenes, and two alkynes). The Natural Bond Theory and the Non-Covalent Interaction Index analyses of the noncovalent interaction energies fail to predict the experimental major regioisomer. Employing global cDFT descriptors or local ones such as the Fukui function and dual descriptor yields similarly inaccurate predictions. Only the local softness pairing, within Pearson's Hard and Soft Acids and Bases principle, constitutes a reliable predictor for the major reaction product. By taking into account an estimator for the steric effects, the correct regioisomer is predicted. Steric effects play a major role in driving the regioselectivity, as was corroborated by energy decomposition analysis of the transition states.

In silico strategy for detailing the binding modes of a novel family of peptides proven as ghrelin receptor agonists.

Ghrelin is a peptide hormone involved in multiple functions, including growth hormone release stimulation, food intake regulation, and metabolic and cytoprotective effect. A novel family of peptides with internal cycles was designed as ghrelin analogs and the biological activity of two of them (A228 and A233) was experimentally studied in-depth. In this work, an in silico strategy was developed for describing and assessing the binding modes of A228 and A233 to GHS-R1a (ghrelin receptor) comparing it with ghrelin and GHRP-6 peptides. Several reported structures of different G protein coupled receptors were used as templates, to obtain a good quality model of GHS-R1a. The best model was selected by preliminary molecular docking with ghrelin and GHRP-6. Docking was used to estimate peptide orientations in the binding site of the best model, observing a superposition of its N-terminal and its first aromatic residue. To test the complex stability in time, the C-terminal fragments of each peptide were added and the complexes were inserted a 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) membrane, performing a molecular dynamic simulation for 100 ns using the CHARMM36 force field. Despite of the structural differences, the studied peptides share a common binding mode; the N-terminal interacts with E124 and the aromatic residue close to it, with the aromatic cluster (F279, F309, and F312). A preliminary pharmacophore model, consisting in a positive charged amine and an aromatic ring at an approximate distance of 0.79 nm, can be proposed. The results here described could represent a step forward in the efficient search of new ghrelin analogs.

Understanding the disrupting mechanism of the Tau aggregation motif "306 VQIVYK311 " by phenylthiazolyl-hydrazides inhibitors.

Alzheimer's disease is a progressive neurodegenerative disorder characterized by the abnormal processing of the Tau and the amyloid precursor proteins. The unusual aggregation of Tau is based on the formation of intermolecular ß-sheets through two motifs: 275 VQIINK280 and 306 VQIVYK311 . Phenylthiazolyl-hydrazides (PTHs) are capable of inhibiting/disassembling Tau aggregates. However, the disaggregation mechanism of Tau oligomers by PTHs is still unknown. In this work, we studied the disruption of the oligomeric form of the Tau motif 306 VQIVYK311 by PTHs through molecular docking, molecular dynamics, and free energy calculations. We predicted hydrophobic interactions as the major driving forces for the stabilization of Tau oligomer, with V306 and I308 being the major contributors. Nonpolar component of the binding free energy is essential to stabilize Tau-PTH complexes. PTHs disrupted mainly the van der Waals interactions between the monomers, leading to oligomer destabilization. Destabilization of full Tau filament by PTHs and emodin was not observed in the sampled 20 ns; however, in all cases, the nonpolar component of the binding free energy is essential for the formation of Tau filament-PTH and Tau filament-emodin. These results provide useful clues for the design of more effective Tau-aggregation inhibitors.

Quality Threshold Clustering of Molecular Dynamics: A Word of Caution.

Clustering Molecular Dynamics trajectories is a common analysis that allows grouping together similar conformations. Several algorithms have been designed and optimized to perform this routine task, and among them, Quality Threshold stands as a very attractive option. This algorithm guarantees that in retrieved clusters no pair of frames will have a similarity value greater than a specified threshold, and hence, a set of strongly correlated frames are obtained for each cluster. In this work, it is shown that various commonly used software implementations are flawed by confusing Quality Threshold with another simplistic well-known clustering algorithm published by Daura et al. (Daura, X.; van Gunsteren, W. F.; Jaun, B.; Mark, A. E.; Gademann, K.; Seebach, D. Peptide Folding: When Simulation Meets Experiment. Angew. Chemie Int. Ed. 1999, 38 (1/2), 236-240). Daura's algorithm does not impose any quality threshold for the frames contained in retrieved clusters, bringing unrelated structural configurations altogether. The advantages of using Quality Threshold whenever possible to explore Molecular Dynamic trajectories is exemplified. An in-house implementation of the original Quality Threshold algorithm has been developed in order to illustrate our comments, and its code is freely available for further use by the scientific community.

Role of Augmented Basis Sets and Quest for ab Initio Performance/Cost Alternative to Kohn-Sham Density Functional Theory.

Following a previous work, we have assessed the feasibility of MP2/CBS(d, t) as an alternative to state-of-the-art density functionals. The effect of using augmented basis sets is here tested on the 76 barrier heights and 10 isomerization reactions previously utilized. Moreover, calculations for 20 sets of the GMTKN24 database for thermochemistry, kinetics, and noncovalent interactions have been performed. For the density functional theory calculations, M06-2X and B3LYP-D3 functionals are utilized as two representative functionals, while MP2 and CCSD(T) methods are employed as the ab initio counterparts. The results show that MP2 calculations perform similarly to the ones obtained with M06-2X insofar as accuracy and computational cost are concerned. For all methods, the use of augmented basis sets yields enhanced results for anionic systems when compared with the ones from non-augmented bases. Otherwise, the basis-set change effect is found to be minimal. It is therefore concluded that the use of large basis sets is unjustified when facing the increase in computational cost.

Conceptual DFT analysis of the regioselectivity of 1,3-dipolar cycloadditions: nitrones as a case of study.

The regioselectivity of the 1,3-dipolar cycloaddition of a model nitrone with a set of dipolarophiles, presenting diverse electronic effects, is analyzed using conceptual density functional theory (DFT) methods. We deviate from standard approaches based on frontier molecular orbitals and formulations of the local hard/soft acid/base principle and use instead the dual descriptor. A detailed analysis is carried out to determine the influence of the way to calculate the dual descriptor, the computational procedure, basis set and choice of method to condensate the values of this descriptor. We show that the qualitative regioselectivity predictions depend on the choice of "computational conditions", something that indicates the danger of using black-box computational set-ups in conceptual DFT studies.

Assessing How Correlated Molecular Orbital Calculations Can Perform versus Kohn-Sham DFT: Barrier Heights/Isomerizations.

To assess the title issue, 38 hydrogen transfer barrier heights and 38 non-hydrogen transfer barrier heights/isomerizations extracted from extensive databases have been considered, in addition to 4 2 p-isomerization reactions and 6 others for large organic molecules. All Kohn-Sham DFT calculations have employed the popular M06-2X functional, whereas the correlated molecular orbital (MO)-based ones are from single-reference MP2 and CCSD(T) methods. They have all utilized the same basis sets, with raw MO energies subsequently extrapolated to the complete basis set limit without additional cost. MP2 calculations are found to be as cost-effective as DFT ones and often slightly more, while showing a satisfactory accuracy when compared with the reference data. Although the focus is on barrier heights, the results may bear broader implications, in that one may see successes and difficulties of DFT when compared with traditional MO theories for the same data.

Integrating sampling techniques and inverse virtual screening: toward the discovery of artificial peptide-based receptors for ligands.

A novel heuristic using an iterative select-and-purge strategy is proposed. It combines statistical techniques for sampling and classification by rigid molecular docking through an inverse virtual screening scheme. This approach aims to the de novo discovery of short peptides that may act as docking receptors for small target molecules when there are no data available about known association complexes between them. The algorithm performs an unbiased stochastic exploration of the sample space, acting as a binary classifier when analyzing the entire peptides population. It uses a novel and effective criterion for weighting the likelihood of a given peptide to form an association complex with a particular ligand molecule based on amino acid sequences. The exploratory analysis relies on chemical information of peptides composition, sequence patterns, and association free energies (docking scores) in order to converge to those peptides forming the association complexes with higher affinities. Statistical estimations support these results providing an association probability by improving predictions accuracy even in cases where only a fraction of all possible combinations are sampled. False positives/false negatives ratio was also improved with this method. A simple rigid-body docking approach together with the proper information about amino acid sequences was used. The methodology was applied in a retrospective docking study to all 8000 possible tripeptide combinations using the 20 natural amino acids, screened against a training set of 77 different ligands with diverse functional groups. Afterward, all tripeptides were screened against a test set of 82 ligands, also containing different functional groups. Results show that our integrated methodology is capable of finding a representative group of the top-scoring tripeptides. The associated probability of identifying the best receptor or a group of the top-ranked receptors is more than double and about 10 times higher, respectively, when compared to classical random sampling methods.

Pyrrolyl-Silicon Compounds as Precursors for Donor-Acceptor Systems Stabilized by Noncovalent Interactions.

Pyrrolyl-silicon compounds were investigated by different theoretical approaches. Model monomers consisted of a pyrrole ring N-substituted with silylmethoxy and silylhydroxy end groups through a propyl chain spacer, designated as PySi and PySiOH. Geometrical, vibrational, and electronic properties, as well as chemical reactivity, are discussed and compared with pyrrole (Py) and N-propylpyrrole (N-PrPy) that were studied in parallel for reference purposes and methods validation. The electronic distribution between PySi and PySiOH differs importantly, the former being an electron donor, as Py and N-PrPy. Conversely, PySiOH presents donor-acceptor character with the LUMO energy level localized on the silanol end group. Global and local reactivity descriptors predict PySiOH more reactive than PySi with two preferential reactive sites: electron-rich Py ring and electron-deficient silanol group. On the basis of experimental studies, oligomers of PySiOH linked α-α' via Py rings (α-α'PynSiOH, n = 2, 3) were considered as model molecules of hydrolyzed PySi. The most stable structures were derived from randomly generated α-α'PynSiOH that were optimized at semiempirical AM1 and refined with M05-2X/6-31G(d,p). Conformational analysis of dimer and trimer structures points to stability enhanced by molecular packing. Nonetheless, NBO and RDG results indicate that oligomer stability is dictated by the cooperative contribution of hydrogen bonding between silanol end groups and dispersive vdW interactions between silanol and the π system of the Py ring. The latter interaction resulting from electron delocalization induced by an electron-deficient silanol group seems to determine the smaller gap energy of T-shaped OH-π arrangements. The theoretical findings support the peculiar chemical behavior revealed by experiment.

Interaction of brassinolide with essential amino acid residues: a theoretical approach.

The interaction of the most active natural brassinosteroid, brassinolide, with the twenty natural amino acids is studied applying the multiple minima hypersurface method to model the molecular interactions explicitly. The resulting thermodynamic data gives useful information about the amino acids with the greatest association for brassinolide and the stabilities of such complexes. Density functional theory (DFT) optimizations were further carried out to test the performance of semiempirical calculations. Additional calculations with a more accurate DFT method were performed to explore the formation of this type of molecular complexes. The semiempirical geometries and stability order of these complexes are in good agreement with the DFT calculations. Each group of amino acids possesses a preferential zone of interaction with brassinolide, forming the polar-charged amino acids the most stable complexes. This study could contribute to future investigations of the interaction of brassinosteroids with the receptor protein in plants.

An approach to hydration of model silica materials by exploring their multiple minima hypersurfaces. The role of entropy of association.

The influence of molecular water on the structure and formation of silica clusters is modeled with the use of the MMH (multiple minima hypersurfaces) approach. It combines quantum chemical Hamiltonians for the calculation of the internal energy with statistical modeling and formulas for the calculation of thermodynamic functions of association. The structures of the most probable clusters of hydration and some properties of the association with water are proposed. Different simple structures are calculated to consider the entropy of association in place of the simpler approach of a single "global minimum". Ab initio, DFT, and semiempirical calculations of the structures and relevant reactions of silica fragments are also reported, confirming the reliability of the results on very different grounded quantum mechanical methods. Particularly, it has been shown that semiempirical PM3 Hamiltonian is reliable for silica cluster calculations of this kind, in comparison with accurate ab initio SCF and other DFT calculations. Apparently, the well-known systematic failures of this Hamiltonian are absent in this kind of structures and interactions. The increasing hydrophobic character of neutral silica clusters appears as a result of the free energies of association, and therefore, it originates on entropy. It is remarkable that the simple global minimum approach currently used in works of molecular modeling on this kind of compounds, where several hydrogen bridges have a place to exist with several different conformations, must be taken with caution given the remarkable entropy of association of such systems.

In silico study of the human rhodopsin and meta rhodopsin II/S-arrestin complexes: impact of single point mutations related to retina degenerative diseases.

We propose two models of the human S-arrestin/rhodopsin complex in the inactive dark adapted rhodopsin and meta rhodopsin II form, obtained by homology modeling and knowledge based docking. First, a homology model for the human S-arrestin was built and validated by molecular dynamics, showing an average root mean square deviation difference from the pattern behavior of 0.76 A. Then, combining the human S-arrestin model and the modeled structure of the two human rhodopsin forms, we propose two models of interaction for the human S-arrestin/rhodopsin complex. The models involve two S-arrestin regions related to the N domain (residues 68-78; 170-182) and a third constituent of the C domain (248-253), with the rhodopsin C terminus (330-348). Of the 22 single point mutations related to retinitis pigmentosa and congenital night blindness located in the cytoplasmatic portion of rhodopsin or in S-arrestin, our models locate 16 in the interaction region and relate two others to possible dimer formation. Our calculations also predict that the light activated complex is more stable than the dark adapted rhodopsin and, therefore, of higher affinity to S-arrestin.

Un modelo computacional de reacciones relacionadas con el mal de Alzheimer en el nivel molecular / A computational model of reactions related to Alzheimer's disease at the molecular level

El desarrollo tecnológico de los medios de cómputo ha hecho posible que se estudien sistemas cada vez mayores y con mayor grado de exactitud. El método empleado es el llamado semiempírico con la parametrización PM3 implementado en el programa MOPAC. En este trabajo se modeló la ruptura de los diferentes enlaces del carbono a, así como la del enlace peptídico. Los cálculos fueron hechos en procesadores Pentium Pro (200 MHz, 64 bytes RAM), Dual Pentium II (233 MHz, 128 bytes RAM), Dual Pentium Pro (200 MHz, 128 bytes RAM). La modelación de las reacciones radicálicas de los aminoácidos constituyentes del fragmento activo del péptido b-amiloide arrojó que era posible la reacción de formación de radicales libres y la ulterior reactividad de estos, que puede conducir a la degradación oxidativa de moléculas circundantes. Esta generación de radicales libres es significativa en las posiciones Ca y la asparagina aparece como el resto más activo