Abstract

Recent years have witnessed utilization of modern machine learning approaches for predicting properties of material using available datasets. However, to identify potential candidates for material discovery, one has to systematically scan through a large chemical space and subsequently calculate the properties of all such samples. On the other hand, generative methods are capable of efficiently sampling the chemical space and can generate molecules/materials with desired properties. In this study, we report a deep learning based inorganic material generator (DING) framework consisting of a generator module and a predictor module. The generator module is developed based upon conditional variational autoencoders (CVAE) and the predictor module consists of three deep neural networks trained for predicting enthalpy of formation, volume per atom and energy per atom chosen to demonstrate the proposed method. The predictor and generator modules have been developed using a one hot key representation of the material composition. A series of tests were done to examine the robustness of the predictor models, to demonstrate the continuity of the latent material space, and its ability to generate materials exhibiting target property values. The DING architecture proposed in this paper can be extended to other properties based on which the chemical space can be efficiently explored for interesting materials/molecules.

Abstract

The computationally expensive nature of ab initio molecular dynamics simulations severely limits its ability to simulate large system sizes and long time scales, both of which are necessary to imitate experimental conditions. In this work, we explore an approach to make use of the data obtained using the quantum mechanical density functional theory (DFT) on small systems and use deep learning to subsequently simulate large systems by taking liquid argon as a test case. A suitable vector representation was chosen to represent the surrounding environment of each Ar atom, and a -NetFF machine learning model where, the neural network was trained to predict the di↵erence in resultant forces obtained by DFT and classical force fields was introduced. Molecular dynamics simulations were then performed using forces from the neural network for various system sizes and time scales depending on the properties we calculated. A comparison of properties obtained from the classical force field and the neural network model was presented alongside available experimental data to validate the proposed method.

Abstract

In recent times, our healthcare system is being challenged by many drug-resistant microorganisms and ageing-associated diseases for which we do not have any drugs or drugs with poor therapeutic profile. With pharmaceutical technological advancements, increasing computational power and growth of related biomedical fields, there have been dramatic increase in the number of drugs approved in general, but still way behind in drug discovery for certain class of diseases. Now, we have access to bigger genomics database, better biophysical methods, and knowledge about chemical space with which we should be able to easily explore and predict synthetically feasible compounds for the lead optimization process. In this chapter, we discuss the limitations and highlights of currently available computational methods used for protein–ligand binding affinities estimation and this includes force-field, ab initio electronic structure theory and machine learning approaches. Since the electronic structure-based approach cannot be applied to systems of larger length scale, the free energy methods based on this employ certain approximations, and these have been discussed in detail in this chapter. Recently, the methods based on electronic structure theory and machine learning approaches also are successfully being used to compute protein–ligand binding affinities and other pharmacokinetic and pharmacodynamic properties and so have greater potential to take forward computer-aided drug discovery to newer heights

Abstract

Hydroxymethylbilane synthase (HMBS), the third enzyme in the heme biosynthesis pathway, catalyzes the formation of 1-hydroxymethylbilane (HMB) by a stepwise polymerization of four molecules of porphobilinogen (PBG) using the dipyrromethane (DPM) cofactor. The mechanism by which HMBS polymerizes four units of PBG has not been elucidated to date. In vitro and in silico studies on HMBS have suggested certain residues with catalytic importance, but their specific role in the catalysis is unclear. To understand the catalytic mechanism of HMBS, quantum mechanical (QM) calculations were performed on model systems obtained from the active site of the human HMBS enzyme. The addition of one molecule of PBG to the DPM cofactor is carried out in four steps: (1) protonation of the substrate, PBG; (2) deamination of PBG; (3) electrophilic addition of the deaminated substrate to the terminal pyrrole ring of the enzyme-bound DPM cofactor and (4) deprotonation of the carbon atom at the a-position of the second ring of DPM. Based on the energy profiles from the QM calculations on cluster models, R26 is proposed to be the best suitable proton donor to the PBG moiety, which aids in the deamination of the substrate. During the electrophilic addition step, the intermediate formed is stabilized by the carboxylate side chain of the D99 residue. In the final deprotonation step, an extra proton from the second ring of DPM is transferred to R26 via the carboxylate side chain of D99, thus completing one cycle of the catalytic mechanism. The residues in the cluster model seem to play an important role in obtaining accurate energy barriers. All the stationary points along the reaction pathway have been characterized using QM calculations. The rate limiting step for the complete mechanism is found to be the deamination of the PBG moiety. The results of this study provide a detailed understanding of the catalytic mechanism and would help design future studies aimed at modulating the activity of HMBS

Abstract

Understanding the structure-function relationships of RNA has become increasingly important given the realization of its functional role in various cellular processes. Chemical denaturation of RNA by urea has been shown to be beneficial in investigating RNA stability and folding. Elucidation of the mechanism of unfolding of RNA by urea is important for understanding the folding pathways. In addition to studying denaturation of RNA in aqueous urea, it is important to understand the nature and strength of interactions of the building blocks of RNA. In this study, a systematic examination of the structural features and energetic factors involving interactions between nucleobases and urea is presented. Results from molecular dynamics (MD) simulations on each of the five DNA/RNA bases in water and eight different concentrations of aqueous urea, and free energy calculations using the thermodynamic integration method are presented. The interaction energies between all the nucleobases with the solvent environment and the transfer free energies become more favorable with respect to increase in the concentration of urea. Preferential interactions of urea versus water molecules with all model systems determined using Kirkwood-Buff integrals and two-domain models indicate preference of urea by nucleobases in comparison to water. The modes of interaction between urea and the nucleobases were analyzed in detail. In addition to the previously identified hydrogen bonding and stacking interactions between urea and nucleobases that stabilize the unfolded states of RNA in aqueous solution, NH-π interactions are proposed to be important. Dynamic properties of each of these three modes of interactions have been presented. The study provides fundamental insights into the nature of interaction of urea molecules with nucleobases and how it disrupts nucleic acids.

Abstract

Understanding synergistic effect by calculating electronic structure is essential to fine-tune the catalytic properties of bimetallic nanoalloy clusters which might be used for design of novel efficient catalysts. Density functional theory PBE0 calculations were performed to investigate the structure and energetics of various intermediates involved in the CO oxidation reaction catalyzed by Au3-xYx (x= 0–3 and Y denotes Ag or, Pt or, Pd) trimeric clusters through two possible pathways: Eley-Rideal (ER) and Langmuir-Hinshelwood (LH). The results of this investigation show that the catalytic behavior of the nanocluster highly depends on its composition and the reaction site taken into consideration. The most active reaction centres of goldsilver, gold-palladium, gold-platinum clusters are gold, palladium and platinum atoms respectively. The gold-silver clusters and AuPt2 prefer ER mechanism whereas, gold-palladium and Au2Pt selectively favour LH mechanism in comparison to the other. Bimetallic clusters, in general, are more efficient in comparison to their pristine mono-metallic counterparts, in activating the OO bond for the reaction and have relatively easy CO2 dissociation. Overall results indicate that the alloyed clusters could potentially have a better catalytic activity as compared to pure gold clusters for CO oxidation at low temperatures.

Abstract

We developed a set of conformationally locked molecules each of which makes a single CH⋯O H-bond/short contact and has different electron density at the acceptor oxygen atom. The downfield shift of the 1H NMR signals due to the hydrogen involved in the CH⋯O H-bond varied from 0.93–1.6 ppm, and the magnitude of Δδ is in correlation with the hybridization state of the acceptor oxygen and with the CH⋯O H-bond strengths quantified using a computational method.

Abstract

Urea-assisted denaturation of protein and RNA has been shown to be a valuable tool to study their stabilities and folding phenomena. It has been shown that stacking interactions between nucleobases and urea are one of the driving forces of denaturation. In this study, the ability of urea to form unconventional stacking interactions with RNA bases is investigated by performing high-level quantum calculations (RI-MP2/aug-ccpVDZ level) on a few thousands of model systems. Four systems were considered based on the RNA nucleobases (GUA, ADE, CYT, and URA) for the investigation. For each system, a set of models were designed to study the role of hetero-atoms/groups of the nucleobases on stacking interactions with urea moiety with respect to every possible pair. Several plane-parallel complexes were generated with urea on top of aromatic systems to exhaustively study all possible factors for urea-nucleobases stacking interactions. Energy decomposition analysis (EDA), atoms in molecules (AIM) and natural bond orbital (NBO) analysis were performed to gain better insights on non-covalent stacking interactions. Dispersion component was found to be heavily stabilizing, while the EHF was found to be repulsive for all the four systems indicating lack of hydrogen bonding (HB) type interactions and presence of dispersion type interactions. Amide and carbonyl groups of urea molecule were found to play a major role in favourable stacking interactions. We demonstrate that along with functional groups present on the nucleobases, the orientation of urea molecules plays a vital role in stabilizing the urea-nucleobase non-covalent interactions. The proposed study quantifies and provides a comprehensive theoretical description of urea nucleobase unconventional stacking interactions which helps to unravel urea driven RNA unfolding mechanism.

Abstract

An understanding of the determinants of the thermal stability of thermostable proteins is expected to enable design of enzymes that can be employed in industrial biocatalytic processes carried out at high temperatures. A major factor that has been proposed to stabilize thermostable proteins is the high occurrence of salt bridges. The current study employs free energy calculations to elucidate the thermodynamics of the formation of salt bridge interactions and the temperature dependence, using acetate and methylguanidium ions as model systems. Three different orientations of the methylguanidinium approaching the carboxylate group have been considered for obtaining the free energy profiles. The association of the two ions becomes more favorable with an increase in temperature. The desolvation penalty corresponding to the association of the ion pair is the lowest at high temperatures. The occurrence of …

Abstract

Gold clusters are currently regarded as new-generation catalysts owing to their exceptional efficiency in accelerating several classes of reactions. Density functional theory (DFT) is the method of choice for the investigation of energy pathways of reactions assisted by metal nanoparticles due to their computational efficiency. However, the reliability of such theoretical studies depends to a large extent on the choice of the DFT functional used. In the present work, the performance of a series of DFT-based functionals to accurately model the prototypical CO oxidation reaction catalyzed by a cluster has been examined by comparing the results with those obtained from high-level ab initio CCSD(T) method. This comparison study has been carried along the two possible pathways [Eley–Rideal (ER) and the Langmuir–Hinshelwood (LH)]. No significant differences among the DFT functionals were observed in …