Ab Initio Protocol Research Articles

Interconnection between Polarization-Detected and Population-Detected Signals: Theoretical Results and Ab Initio Simulations.

Most of spectroscopic signals are specified by the nonlinear laser-induced polarization. In recent years, population-detection of signals becomes a trend in femtosecond spectroscopy. Polarization-detected (PD) and population-detected signals are fundamentally different, because they are determined by photoinduced processes acting on disparate time scales. In this work, we consider the fluorescence-detected (FD) N-wave-mixing (NWM) signal as a representative example of population-detected signals, derive a rigorous expression for this signal, and discuss its approximate variants suitable for numerical simulations. This leads us to the definition of the phenomenological FD (PFD) signal, which contains as a special case all definitions of FD signals available in the literature. Then we formulate and prove the population-polarization equivalence (PPE) theorem, which states that PFD NWM signals produced by (possibly strong) laser pulses can be evaluated as conventional PD signals in which the effective polarization is determined by the PFD transition dipole moment operator. We use the PPE theorem for the construction of the ab initio protocol for the simulation of PFD 4WM signals. As an example, we calculate electronic two-dimensional (2D) PFD spectra of the gas-phase pyrazine and compare them with the corresponding PD 2D spectra.

High-level Ab InitioEvidence of Bipyramidal Cu5Clusters as FluxionalJahn-Teller Molecules.

Novel highly selective synthesis techniques have enable the production of atomically precise monodisperse metal clusters (AMCs) of subnanometer size. These AMCs exhibit `molecule-like' structures that have distinct physical and chemical properties, significantly different from those of nanoparticles and bulk material. In this work, we study copper pentamer Cu5clusters as model AMCs by applying both density functional theory (DFT) and high-level (wave-function-based) ab initiomethods, including those which are capable of accounting for the multi-state multi-reference character of the wavefunction at the conical intersection between different electronic states and augmenting the electronic basis set till convergence. After assessing the accuracy of a high-level multi-multireference ab initioprotocol for the well-known Cu3case, we apply it to demonstrate that bypiramidal Cu5clusters are distorted Jahn-Teller (JT) molecules. The method is further used to evaluate the accuracy of single-reference approaches, finding that the coupled cluster singles and doubles and perturbative triples CCSD(T) method are very closeto our ab initiopredictions.Finally, we discuss how JT effects and, more generally, conical intersections, are intimately connected to the fluxionality of AMCs, giving them a `floppy' character that ultimately facilitates their interaction with environmental molecules and thus enhances their functioning as catalysts.

Temperature‐Dependent Anharmonic Phonons in Quantum Paraelectric KTaO3 by First Principles and Machine‐Learned Force Fields

AbstractUnderstanding collective phenomena in quantum materials from first principles is a promising route toward engineering materials properties and designing new functionalities. This work examines the quantum paraelectric state, an elusive state of matter characterized by the smooth saturation of the ferroelectric instability at low temperature due to quantum fluctuations associated with anharmonic phonon effects. The temperature‐dependent evolution of the soft ferroelectric phonon mode in the quantum paraelectric KTaO3 in the range 0–300 K is modeled by combining density functional theory (DFT) calculations with the stochastic self‐consistent harmonic approximation assisted by an on‐the‐fly machine‐learned force field. The calculated data show that including anharmonic terms is essential to stabilize the spurious imaginary ferroelectric phonon predicted by DFT in the harmonic approximation, in agreement with experiments. Augmenting the DFT workflow with machine‐learned force fields allows for efficient stochastic sampling of the configuration space using large supercells in a wide temperature range, inaccessible to conventional ab initio protocols. This work proposes a robust computational workflow capable of accounting for collective behaviors involving different degrees of freedom and occurring at large time/length scales, paving the way for precise modeling and control of quantum effects in materials.

Lattice thermal conductivity of 2D nanomaterials: a simple semi-empirical approach.

Extracting reliable information on certain physical properties of materials, such as thermal transport, can be computationally very demanding. Aiming to overcome such difficulties in the particular case of lattice thermal conductivity (LTC) of 2D nanomaterials, we propose a simple, fast, and accurate semi-empirical approach for LTC calculation. The approach is based on parameterized thermochemical equations and Arrhenius-like fitting procedures, thus avoiding molecular dynamics or ab initio protocols, which frequently require computationally expensive simulations. As a proof of concept, we obtain the LTC of some prototypical physical systems, such as graphene (and other 2D carbon allotropes), hexagonal boron nitride (hBN), silicene, germanene, binary, and ternary BNC lattices and two examples of the fullerene network family. Our obtained values are in good agreement with other theoretical and experimental estimations, nonetheless, being derived in a rather straightforward way, at a fraction of the usual computational cost.

Rosetta FlexPepDock to predict peptide-MHC binding: An approach for non-canonical amino acids.

Computation methods that predict the binding of peptides to MHC-I are important tools for screening and identifying immunogenic antigens and have the potential to accelerate vaccine and drug development. However, most available tools are sequence-based and optimized only for peptides containing the twenty canonical amino acids. This omits a large number of peptides containing non-canonical amino acids (NCAA), or residues that undergo varied post-translational modifications such as glycosylation or phosphorylation. These modifications fundamentally alter peptide immunogenicity. Similarly, existing structure-based methods are biased towards canonical peptide backbone structures, which may or may not be preserved when NCAAs are present. Rosetta FlexPepDock ab-initio is a structure-based computational protocol able to evaluate peptide-receptor interaction where no prior information of the peptide backbone is known. We benchmarked FlexPepDock ab-initio for docking canonical peptides to MHC-I, and illustrate for the first time the method's ability to accurately model MHC-I bound epitopes containing NCAAs. FlexPepDock ab-initio protocol was able to recapitulate near-native structures (≤1.5Å) in the top lowest-energy models for 20 out of 25 cases in our initial benchmark. Using known experimental binding affinities of twenty peptides derived from an influenza-derived peptide, we showed that FlexPepDock protocol is able to predict relative binding affinity as Rosetta energies correlate well with experimental values (r = 0.59, p = 0.006). ROC analysis revealed 80% true positive and a 40% false positive rate, with a prediction power of 93%. Finally, we demonstrate the protocol's ability to accurately recapitulate HLA-A*02:01 bound phosphopeptide backbone structures and relative binding affinity changes, the theoretical structure of the lymphocytic choriomeningitis derived glycosylated peptide GP392 bound to MHC-I H-2Db, and isolevuglandin-adducted peptides. The ability to use non-canonical amino acids in the Rosetta FlexPepDock protocol may provide useful insight into critical amino acid positions where the post-translational modification modulates immunologic responses.

Combining Machine Learning Approaches and Accurate Ab Initio Enhanced Sampling Methods for Prebiotic Chemical Reactions in Solution.

The study of the thermodynamics, kinetics, and microscopic mechanisms of chemical reactions in solution requires the use of advanced free-energy methods for predictions to be quantitative. This task is however a formidable one for atomistic simulation methods, as the cost of quantum-based ab initio approaches, to obtain statistically meaningful samplings of the relevant chemical spaces and networks, becomes exceedingly heavy. In this work, we critically assess the optimal structure and minimal size of an ab initio training set able to lead to accurate free-energy profiles sampled with neural network potentials. The results allow one to propose an ab initio protocol where the ad hoc inclusion of a machine-learning (ML)-based task can significantly increase the computational efficiency, while keeping the ab initio accuracy and, at the same time, avoiding some of the notorious extrapolation risks in typical atomistic ML approaches. We focus on two representative, and computationally challenging, reaction steps of the classic Strecker-cyanohydrin mechanism for glycine synthesis in water solution, where the main precursors are formaldehyde and hydrogen cyanide. We demonstrate that indistinguishable ab initio quality results are obtained, thanks to the ML subprotocol, at about 1 order of magnitude less of computational load.

Modeling, molecular dynamics and docking studies of a full-length Echinococcus granulosus 2DBD nuclear receptor

Nuclear receptors are ligand-activated transcription factors capable of regulating the expression of complex gene networks. The family includes seven subfamilies of protein with a wide phylogenetic distribution. A novel subfamily with two DNA-binding domains (2DBDs) has been first reported in Schistosoma mansoni (Platyhelminth, Trematoda). Employing an ab initio protocol and homology modeling methods, the full-length 3D structure of the Eg2DBDα.1 nuclear receptor from Echinococcus granulosus (Platyhelminth, Cestoda) was generated. The model analysis reveals the presence of the conserved three-layered alpha-helical sandwich structure in the ligand binding domain, and a particularly long and flexible hinge region. Molecular dynamics simulations were performed previous to dock a conformational library of fatty acids and retinoic acids. Our results indicate that oleic and linoleic acids are suitable ligands to this receptor. The ligand-protein complex is stabilized mainly by hydrogen bonds and hydrophobic interactions. The fact that 2DBD nuclear receptors have not been identified in vertebrates confers particular interest to these nuclear receptors, not only concerning their structure and function but as targets of new anthelmintic drugs. Communicated by Ramaswamy H. Sarma

Quantum Mechanical-Based Stability Evaluation of Crystal Structures for HIV-Targeted Drug Cabotegravir.

The long-acting parenteral formulation of the HIV integrase inhibitor cabotegravir (GSK744) is currently being developed to prevent HIV infections, benefiting from infrequent dosing and high efficacy. The crystal structure can affect the bioavailability and efficacy of cabotegravir. However, the stability determination of crystal structures of GSK744 have remained a challenge. Here, we introduced an ab initio protocol to determine the stability of the crystal structures of pharmaceutical molecules, which were obtained from crystal structure prediction process starting from the molecular diagram. Using GSK744 as a case study, the ab initio predicted that Gibbs free energy provides reliable further refinement of the predicted crystal structures and presents its capability for becoming a crystal stability determination approach in the future. The proposed work can assist in the comprehensive screening of pharmaceutical design and can provide structural predictions and stability evaluation for pharmaceutical crystals.

High-level thermochemistry for the octasulfur ring: A converged coupled cluster perspective for a challenging second-row system

Sulfur clusters are challenging targets for high-level ab initio procedures. The heat of formation of the most common and energetically stable S8 allotrope (α-sulfur) has not been the subject of a high-level ab initio investigation. We apply the Weizmann-n computational thermochemistry protocols to the S8 sulfur cluster. We show that calculating the heat of formation with sub-chemical accuracy requires accurate treatment of post-CCSD(T), core-valence, scalar relativistic, and zero-point vibrational energy contributions. At the relativistic, all-electron CCSDT(Q)/CBS level of theory we obtain an enthalpy of formation at 0 K of ∆fH°0 = 24.44 kcal mol–1, and at 298 K of ∆fH°298 = 23.51 kcal mol–1. These values suggest that the experimental values from Gurvich (∆fH°0 = 25.1 ± 0.5 kcal mol–1) and JANAF (∆fH°0 = 24.95 ± 0.15 and ∆fH°298 = 24.00 ± 0.15 kcal mol–1) represent overestimations and should be revised downward by 0.5–0.7 kcal mol–1. We also show that computationally economical composite ab initio protocols such as G4, G4(MP2), and CBS-QB3 are unable to achieve chemical accuracy relative to our best CCSDT(Q)/CBS heat of formation for S8.

Ab initio diagnosis of isomerization pathway of diphosphene and diphosphinylidene

An appositeness of recently developed multireference perturbation theory coupled with an improved virtual orbitals complete active space configuration interaction reference function (IVO-SSMRPT) has been examined here against strongly correlated molecular systems emerging from isomerization of diphosphene and diphosphinylidene. The IVO-SSMRPT is an interesting alternative to the widely used MRPT methods which avoid some of the objections of the conventional MRPTs. IVO-BWMRPT furnish a reliable description of near-degeneracy between occupied and unoccupied orbitals at the twisted geometry where the wave function receives biradical character. Overall, the IVO-SSMRPT results are in good agreement with the more expensive state-of-the-art ab initio calculations discloses the accomplishment of the IVO-SSMRPT method. The IVO-SSMRPT method can be considered as an economical MR ab initio protocol that renders a good compromise between accuracy and computational expense.

OH-Initiated Reactions of p-Coumaryl Alcohol Relevant to the Lignin Pyrolysis. Part I. Potential Energy Surface Analysis ⊥.

Cinnamyl alcohols such as p-coumaryl alcohol ( p-CMA) are lignin models and precursors (monolignols) and the most important primary products of lignin pyrolysis. However, the detection of monomers is not straightforward since they either undergo secondary transformations or repolymerize to contribute to the char formation. Both concerted-molecular and free-radical pathways are involved in these processes. Our recent fundamentally based theoretical and low-temperature matrix-isolation-EPR studies of cinnamyl alcohols highlighted the role of side-chain reactivity in diversity of pyrolysis products and provided a network of the chemically activated H + p-CMA reactions ( Asatryan J. Phys. Chem. A, 2017 , 121 , 3352 - 3371 ). The readily available hydroxyl radicals also can trigger a cascade of free-radical processes. Here, we present a comprehensive potential energy surface (PES) analysis of the OH + p-CMA reaction using various DFT and ab initio protocols. Since the p-CMA involves both an alkyl OH-group and a side-chain double bond, the title reaction can also serve as a relevant model for reactions of unsaturated alcohols with hydroxyl radicals to form various oxygenates including polyhydric alcohols which are abundant in nature. The newly identified pathways suggest certain alternatives to the known radical reactions. Of particular interest are the roaming-like low-energy dehydration reactions to generate a variety of O- and C-centered intermediate radicals, which are primarily transformed into the phenolic compounds observed in pyrolysis experiments. Several concerted unimolecular decomposition pathways for p-CMA are also revealed, not considered previously, such as the migration of terminal OH-group, and/or its splitting over the ipso-C and ortho-C atoms of the benzene ring to form bicyclic oxispiro- and chromene compounds represented in natural lignin.

Structural and biophysical characterization of cajanus cajan protease inhibitor

Context:A large number of studies have proven that Protease inhibitors (PIs), specifically serine protease inhibitors, show immense divergence in regulation of proteolysis by targeting their specific proteases and hence, they play a key role in healthcare.Objective:We aimed to access in-vitro anticancer potential of PI from Cajanus cajan (CCPI). Also, crystallization of CCPI was targetted alongwith structure determination and its structure-function relationship.Materials and Methods:CCPI was purified from Cajanus cajan seeds by chromatographic techniques. The purity and molecular mass was determined by SDS-PAGE. Anticancer potential of CCPI was determined by MTT assay in normal HEK and cancerous A549 cells. The crystallization screening of CCPI was performed by commercially available screens. CCPI sequence was subject to BLASTp with homologous PIs. Progressive multiple alignment was performed using clustalw2 and was modelled using ab initio protocol of I-TASSER.Results:The results showed ~14kDa CCPI was purified in homogeneity. Also, CCPI showed low cytotoxic effects of in HEK i.e., 27% as compared with 51% cytotoxicity in A549 cells. CCPI crystallized at 16°C using 15% PEG 6000 in 0.1M potassium phosphate buffer (pH 6.0) in 2-3weeks as rod or needles visualized as clusters under the microscope. The molecular modelling revealed that it contains 3 beta sheets, 3 beta hairpins, 2 β-bulges, 6 strands, 3 helices, 1helix-helix interaction, 41 β-turns and 27 γ-turns.Discussion and Conclusion:The results indicate that CCPI may help to treat cancer in vivo aswell. Also, this is the first report on preliminary crystallization and structural studies of CCPI.

Computed insight into a peptide inhibitor preventing the induced fit mechanism of MurA enzyme from Pseudomonas aeruginosa.

UDP-N-acetylglucosamine enolpyruvyl transferase (MurA) is one of the key enzymes involved in peptidoglycan biosynthesis. The peptide HESFWYLPHQSY (called PEP 1354) is an inhibitor of MurA with an IC50 value of 200μm. In this article, we have used the FlexPepDock ab-initio protocol from the Rosetta program homology modeling and molecular dynamics simulations to analyze, for the first time, the interaction of the PEP 1354 peptide with MurA enzyme from Pseudomonas aeruginosa (MurA-PA). Our modeling results suggest that the peptide binds to the same active site as the natural substrate UDP-N-acetylglucosamine (UNAG). Additionally, the MurA-peptide complex revealed that the peptide seems to prevent the closure of the Pro114-123 loop and, consequently, the open-closed transition of the MurA structure.

Relationship between Electron Affinity and Half-Wave Reduction Potential: A Theoretical Study on Cyclic Electron-Acceptor Compounds.

A high-level ab initio protocol to compute accurate electron affinities and half-wave reduction potentials is presented and applied for a series of electron-acceptor compounds with potential interest in organic electronics and redox flow batteries. The comprehensive comparison between the theoretical and experimental electron affinities not only proves the reliability of the theoretical G3(MP2) approach employed but also calls into question certain experimental measurements, which need to be revised. By using the thermodynamic cycle for the one-electron attachment reaction A+e- →A- , theoretical estimates for the first half-wave reduction potential have been computed along the series of electron-acceptor systems investigated, with maximum deviations from experiment of only 0.2 V. The precise inspection of the terms contributing to the half-wave reduction potential shows that the difference in the free energy of solvation between the neutral and the anionic species (ΔΔGsolv ) plays a crucial role in accurately estimating the electron-acceptor properties in solution, and thus it cannot be considered constant even in a family of related compounds. This term, which can be used to explain the occasional lack of correlation between electron affinities and reduction potentials, is rationalized by the (de)localization of the additional electron involved in the reduction process along the π-conjugated chemical structure.

Combining Evolutionary Information and an Iterative Sampling Strategy for Accurate Protein Structure Prediction.

Recent work has shown that the accuracy of ab initio structure prediction can be significantly improved by integrating evolutionary information in form of intra-protein residue-residue contacts. Following this seminal result, much effort is put into the improvement of contact predictions. However, there is also a substantial need to develop structure prediction protocols tailored to the type of restraints gained by contact predictions. Here, we present a structure prediction protocol that combines evolutionary information with the resolution-adapted structural recombination approach of Rosetta, called RASREC. Compared to the classic Rosetta ab initio protocol, RASREC achieves improved sampling, better convergence and higher robustness against incorrect distance restraints, making it the ideal sampling strategy for the stated problem. To demonstrate the accuracy of our protocol, we tested the approach on a diverse set of 28 globular proteins. Our method is able to converge for 26 out of the 28 targets and improves the average TM-score of the entire benchmark set from 0.55 to 0.72 when compared to the top ranked models obtained by the EVFold web server using identical contact predictions. Using a smaller benchmark, we furthermore show that the prediction accuracy of our method is only slightly reduced when the contact prediction accuracy is comparatively low. This observation is of special interest for protein sequences that only have a limited number of homologs.

High precision quantum-chemical treatment of adsorption: Benchmarking physisorption of molecular hydrogen on graphane.

A multilevel hierarchical ab initio protocol for calculating adsorption on non-conducting surfaces is presented. It employs fully periodic treatment, which reaches local Møller-Plesset perturbation theory of second order (MP2) with correction for the basis set incompleteness via the local F12 technique. Post-MP2 corrections are calculated using finite clusters. That includes the coupled cluster treatment in the local and canonical frameworks (up to perturbative quadruples) and correlated core (with MP2). Using this protocol, the potential surface of hydrogen molecules adsorbed on graphane was computed. According to the calculations, hydrogen molecules are adsorbed on graphane in a perpendicular to the surface orientation with the minimum of the potential surface of around -3.6 kJ/mol located at the distance of 3.85 Å between the bond center of the hydrogen molecule and the mid-plane of graphane. The adsorption sites along the path from the downward-pointing carbon to the ring center of the graphane are energetically virtually equally preferable, which can enable nearly free translations of hydrogen molecules along these paths. Consequently, the hydrogen molecules on graphane most likely form a non-commensurate monolayer. The analysis of the remaining errors reveals a very high accuracy of the computed potential surface with an error bar of a few tenths of a kJ/mol. The obtained results are a high-precision benchmark for further theoretical and experimental studies of hydrogen molecules interacting with graphane.

Assessing the Applicability of Template-Based Protein Docking in the Twilight Zone

The structural modeling of protein interactions in theabsence of close homologous templates is a challenging task. Recently, template-based docking methods have emerged to exploit local structural similarities to help ab-initio protocols provide reliable 3D models for protein interactions. In this work, we critically assess the performance of template-based docking in the twilight zone. Our results show that, while it is possible to find templates for nearly all known interactions, the quality of the obtained models is rather limited. We can increase the precision of the models at expenses of coverage, but it drastically reduces the potential applicability of the method, as illustrated by the whole-interactome modeling of nine organisms. Template-based docking is likely to play an important role in the structural characterization of the interaction space, but we still need to improve the repertoire of structural templates onto which we can reliably model protein complexes.

Accurate Determination of Barrier Height and Kinetics for the F + H2O → HF + OH Reaction

The reaction energy and barrier height of the title reaction are investigated using two high-level ab initio protocols, namely Focal Point Analysis (FPA) and modified High Accuracy Extrapolated Ab Initio Thermochemistry (HEAT) methods. It is concluded from these calculations that despite some multireference character, dynamic electron correlation plays a dominant role near the reaction barrier. Thus, the coupled-cluster method with higher excitations than singles and doubles gives a better description than the multireference configuration interaction method for the barrier height. The FPA and HEAT classical barrier heights, including the spin-orbit and other corrections, are 1.919 and 2.007 kcal/mol, respectively. The rate constants and H/D kinetic isotope effect for the title reaction are determined by semiclassical transition-state theory based on the anharmonic potential energy surface near the saddle point, and the agreement with experiment is excellent. The rate constants are also computed using a quasi-classical trajectory method on a global potential energy surface scaled to the FPA barrier height and a similar level of agreement with experimental data is obtained.

Rapid sampling of local minima in protein energy surface and effective reduction through a multi-objective filter.

BackgroundMany problems in protein modeling require obtaining a discrete representation of the protein conformational space as an ensemble of conformations. In ab-initio structure prediction, in particular, where the goal is to predict the native structure of a protein chain given its amino-acid sequence, the ensemble needs to satisfy energetic constraints. Given the thermodynamic hypothesis, an effective ensemble contains low-energy conformations which are similar to the native structure. The high-dimensionality of the conformational space and the ruggedness of the underlying energy surface currently make it very difficult to obtain such an ensemble. Recent studies have proposed that Basin Hopping is a promising probabilistic search framework to obtain a discrete representation of the protein energy surface in terms of local minima. Basin Hopping performs a series of structural perturbations followed by energy minimizations with the goal of hopping between nearby energy minima. This approach has been shown to be effective in obtaining conformations near the native structure for small systems. Recent work by us has extended this framework to larger systems through employment of the molecular fragment replacement technique, resulting in rapid sampling of large ensembles.MethodsThis paper investigates the algorithmic components in Basin Hopping to both understand and control their effect on the sampling of near-native minima. Realizing that such an ensemble is reduced before further refinement in full ab-initio protocols, we take an additional step and analyze the quality of the ensemble retained by ensemble reduction techniques. We propose a novel multi-objective technique based on the Pareto front to filter the ensemble of sampled local minima.Results and conclusionsWe show that controlling the magnitude of the perturbation allows directly controlling the distance between consecutively-sampled local minima and, in turn, steering the exploration towards conformations near the native structure. For the minimization step, we show that the addition of Metropolis Monte Carlo-based minimization is no more effective than a simple greedy search. Finally, we show that the size of the ensemble of sampled local minima can be effectively and efficiently reduced by a multi-objective filter to obtain a simpler representation of the probed energy surface.

A Probabilistic Fragment-Based Protein Structure Prediction Algorithm

Conformational sampling is one of the bottlenecks in fragment-based protein structure prediction approaches. They generally start with a coarse-grained optimization where mainchain atoms and centroids of side chains are considered, followed by a fine-grained optimization with an all-atom representation of proteins. It is during this coarse-grained phase that fragment-based methods sample intensely the conformational space. If the native-like region is sampled more, the accuracy of the final all-atom predictions may be improved accordingly. In this work we present EdaFold, a new method for fragment-based protein structure prediction based on an Estimation of Distribution Algorithm. Fragment-based approaches build protein models by assembling short fragments from known protein structures. Whereas the probability mass functions over the fragment libraries are uniform in the usual case, we propose an algorithm that learns from previously generated decoys and steers the search toward native-like regions. A comparison with Rosetta AbInitio protocol shows that EdaFold is able to generate models with lower energies and to enhance the percentage of near-native coarse-grained decoys on a benchmark of proteins. The best coarse-grained models produced by both methods were refined into all-atom models and used in molecular replacement. All atom decoys produced out of EdaFold’s decoy set reach high enough accuracy to solve the crystallographic phase problem by molecular replacement for some test proteins. EdaFold showed a higher success rate in molecular replacement when compared to Rosetta. Our study suggests that improving low resolution coarse-grained decoys allows computational methods to avoid subsequent sampling issues during all-atom refinement and to produce better all-atom models. EdaFold can be downloaded from http://www.riken.jp/zhangiru/software/.