Journal of Computational Chemistry最新文献

Modern potential energy surfaces have shifted attention to molecular simulations of chemical reactions. While various methods can estimate rate constants for conformational transitions in molecular dynamics simulations, their applicability to studying chemical reactions remains uncertain due to the high and sharp energy barriers and complex reaction coordinates involved. This study focuses on the thermal cis-trans isomerization in retinal, employing molecular simulations and comparing rate constant estimates based on one-dimensional rate theories with those based on sampling transitions and grid-based models for low-dimensional collective variable spaces. Even though each individual method to estimate the rate passes its quality tests, the rate constant estimates exhibit considerable disparities. Rate constant estimates based on one-dimensional reaction coordinates prove challenging to converge, even if the reaction coordinate is optimized. However, consistent estimates of the rate constant are achieved by sampling transitions and by multi-dimensional grid-based models.

This paper reports the improvement in the efficiency of embedded-cluster model (ECM) calculations in ORCA thanks to the implementation of the fast multipole method. Our implementation is based on state-of-the-art algorithms and revisits certain aspects, such as efficiently and accurately handling the extent of atomic orbital shell pairs. This enables us to decompose near-field and far-field terms in what we believe is a simple and effective manner. The main result of this work is an acceleration of the evaluation of electrostatic potential integrals by at least one order of magnitude, and up to two orders of magnitude, while maintaining excellent accuracy (always better than the chemical accuracy of 1 kcal/mol). Moreover, the implementation is versatile enough to be used with molecular systems through QM/MM approaches. The code has been fully parallelized and is available in ORCA 6.0.

Guanine quadruplexes (GQs) play crucial roles in various biological processes, and understanding their folding pathways provides insight into their stability, dynamics, and functions. This knowledge aids in designing therapeutic strategies, as GQs are potential targets for anticancer drugs and other therapeutics. Although experimental and theoretical techniques have provided valuable insights into different stages of the GQ folding, the structural complexity of GQs poses significant challenges, and our understanding remains incomplete. This study introduces a novel computational protocol for folding an entire GQ from single-strand conformation to its native state. By combining two complementary enhanced sampling techniques, we were able to model folding pathways, encompassing a diverse range of intermediates. Although our investigation of the GQ free energy surface (FES) is focused solely on the folding of the all-anti parallel GQ topology, this protocol has the potential to be adapted for the folding of systems with more complex folding landscapes.

Cisplatin (CDDP) is an effective Platinum (Pt) based anticancer drug used in chemotherapy. However, its effectiveness is limited due to its instability in solvents, along with the side effects it causes due to DNA damage. Nanoparticles (NPs) were developed in vitro to address these issues by loading CDDP into various types of NPs, including metal, lipid, and biological NPs. Citrate was employed as a biocompatible compound in nanomedicine to reduce cytotoxicity and enhance stability. In our study, the physicochemical and electronic properties of CDDP and citrate have been investigated using density functional theory (DFT), with a comparison of their behavior in water and DMSO. Additionally, TD-DFT was applied to analyze the UV–Vis spectra results. Six complexes have been proposed to better understand the interaction between citrate and CDDP. The results demonstrated that the CDDP could form stable complexes with citrate in both water and DMSO, and the considered complexes exhibited UV–Vis spectra within the experiment range. The frontier orbitals, electron densities mapping, and electrostatic potential analysis revealed that complex 5, where citrate di-substituted on two chlorides, is the most likely and effective complex. In summary, our investigation sheds light on the potential of CDDP-citrate complexes to address the limitations of CDDP, offering insights into their stability and interaction in solvents and highlighting the promising efficacy of specific complex formations for future therapeutic applications.

Predicting drug target binding affinity has huge relevance in Modern drug discovery and drug repositioning processes which assist doctors to come up with new drugs or even use the existing drugs for new target proteins. In silico models, using advanced deep learning techniques could further assist these prediction tasks by providing most prominent drug target pairs. Considering these factors, a deep learning based algorithmic framework is developed in this study to support drug target interaction prediction. The proposed SeqDPI model extract the relevant drug and protein features from the one dimensional Sequential representation of the dataset considered using optimized CNN networks that deploy convolutions on varying length of amino acid subsequence's to capture hidden pattern, the convolved drug- protein features obtained are then used as an input to L2 penalized feed forward neural network which matches the local residue patterns in protein classes with molecular fingerprints of drugs to predict the binding strength for all drug target pairs. The proposed model reduces the convolution strain typically encountered in existing in silico models that utilize complex 3D structures of drug protein datasets. The result shows that the SeqDPI model achieves a mean square error MSE of (0.167) across cross validation folds, outperforming baseline models such as KronRLS (0.406), Simboost (0.226), and DeepPS (0.214). Additionally, SeqDPI attains a high CI score of 0.9114 on the benchmark KIBA dataset, demonstrating its statistical significance and computational efficiency compared to existing methods. This gives the relevance and effectiveness of SeqDPI model in accurately predicting binding affinities while working with simpler one-dimensional data, making it a robust and computationally cost-effective solution for drug-target interaction prediction.

The interaction between different metals (M), axial ligands (L), and ring substituents (R) in porphyrins was investigated using density functional theory. Different combinations of iron and cobalt as metal centers; imidazole, chlorine, and an n-heterocyclic carbene (NHC) as axial ligands, and unsubstituted, octaethyl-, and tetraphenyl-porphyrins were explored in their low, intermediate, and high-spin states, alongside oxygen affinity. Remarkably, the n-heterocyclic carbene enhanced the affinity of cobalt porphyrins to oxygen, with binding energies on average 4.4 kcal mol⁻¹ higher than FeP with the same ligand, and 0.78 kcal mol⁻¹ higher than FeP with imidazole. The planarity of the iron tetraphenyl porphyrin with imidazole compared to its ruffled cobalt counterpart is noteworthy in both oxy- and deoxy-forms, highlighting imidazole's stabilizing influence on the porphyrin structure, particularly iron porphyrins, alongside imidazole's stabilizing effect on the affinity to O₂. Despite the significant non-planarity induced by NHC as an axial ligand -regardless of the metal or ring substituent used-, it did not hinder the affinity of CoP to O₂ (14.26 kcal mol⁻¹, on average) as it did with the FeP with NHC (9.88 kcal mol⁻¹, on average). Cobalt porphyrins with n-heterocyclic carbene ligands show promising potential for O₂ activation or oxygen transport applications. The results show the complex interactions between the different parts of metalloporphyrins and highlight the capability of tailoring their affinity to O₂. It also exemplifies the stabilizing effect of imidazole on the porphyrins, providing a very narrow range of binding energies and smaller differences in their geometries.

Combining metalloporphyrins (MPr) and graphene constitutes key composites in the development of photovoltaic devices. Here, we focus on the analysis of the properties of metalloporphyrins/graphene systems by means of the density functional theory (DFT) and its time-dependent (TDDFT) version, focusing on the ground and singlet excited states. Our benchmark analysis concludes that ωB97XD density functional combined with 6-31G(d)/Def2-TZVP basis set is a better-suited method for simulating accurate MPr adsorption on graphene. It is shown that a reduced atomic model where the external organic shell of the structure is removed provides the same resulting optoelectronic properties of the original model, constituting an important speed-up of the calculations when studying porphyrins-derived molecules. We observe that ZnPr provides the highest light harvesting efficiency (LHE) value. In addition, we find out that the adsorption energy increases monotonically with the size of the graphene flake and the highest stability involves the use of graphene comprising above 500 atoms. Besides, CdPr and HgPr keep their properties as photosensitizers when they are bonded to graphene and show promising values in terms of LHE emerging as suitable solar energy harvesters.