Atomic Charge Calculator II: web-based tool for the calculation of partial atomic charges

Extraction of Pyrrole from Its Mixture with n-Hexadecane Using Protic Ionic Liquids

The removal of nitrogen compounds from fuel via the conventional method, which is hydrodenitrogenation, is costly and involves catalysts and energy-intensive conditions (600 K and 300 atm). Recently, ionic liquids (ILs) have emerged as a promising alternative solvent for the denitrogenation of fuel oil. However, certain ILs are expensive and challenging to synthesize, prompting the exploration of protic ionic liquid (PIL) substitutes, which offer similar advantages to ILs. This study utilized the conductor-like screening model for real solvents (COSMO-RS) to predict the phase equilibria for three PILs-triethylammonium p-toluenesulfonate (TEA-TSA), triethylammonium salicylate (TEA-SA) and triethylammonium benzoate (TEA-BZ)-which were subsequently validated through experimental investigations. Liquid-liquid extraction experiments were conducted at 298 K and 1 atm, with pyrrole (serving as the model nitrogen compound) concentrations in n-hexadecane (representing the model fuel) ranging from 10 to 50 wt%. Additionally, the NRTL model effectively correlated the experimental tie lines. The obtained data indicated that TEA-TSA exhibited superior selectivity and distribution ratio compared to TEA-SA and TEA-BZ. All the ternary systems tested displayed positive slopes, suggesting a higher affinity of nitrogen compounds for the PIL. Supporting this observation, interaction energy (ΔE) and excess enthalpy (HE) were employed. The predicted outcomes revealed that TEA-TSA had high ΔE, and all PILs exhibited negative values of HE. The HE calculation underscored the significance of strong hydrogen bond interactions between pyrrole and the PIL for successful extraction.

SHP-1 Variants Broaden the Understanding of pH-Dependent Activities in Protein Tyrosine Phosphatases

The protein tyrosine phosphatase (PTP) SHP-1 plays an important role in both immune regulation and oncogenesis. This enzyme is part of a broader family of PTPs that all play important regulatory roles in vivo. Common to these enzymes is a highly conserved aspartic acid (D421 in SHP-1) that acts as an acid/base catalyst during the PTP-catalyzed reaction. This residue is located on a mobile loop, the WPD-loop, the dynamic behavior of which is intimately connected to the catalytic activity. The SHP-1 WPD-loop variants H422Q, E427A, and S418A have been kinetically characterized and compared to those of the wild-type (WT) enzyme. These variants exhibit limiting magnitudes of k cat ranging from 43 to 77% of the WT enzyme. However, their pH profiles are significantly broadened in the basic pH range. As a result, above pH 6, the E427A and S418A variants have turnover numbers notably higher than those of WT SHP-1. Molecular modeling results indicate that the shifted pH dependencies result primarily from changes in solvation and hydrogen-bonding networks that affect the pK a of the D421 residue, explaining the changes in pH-rate profiles for k cat on the basic side. In contrast, a previous study of a noncatalytic residue variant of the PTP YopH, which also exhibited changes in pH dependency, showed that the catalytic change arose from mutation-induced changes in conformational equilibria of the WPD-loop. This finding and the present study show the existence of distinct strategies for nature to tune the activity of PTPs in particular environments through controlling the pH dependency of catalysis.

Utilizing biological experimental data and molecular dynamics for the classification of mutational hotspots through machine learning

Motivation

Benzo[a]pyrene, a notorious DNA-damaging carcinogen, belongs to the family of polycyclic aromatic hydrocarbons commonly found in tobacco smoke. Surprisingly, nucleotide excision repair (NER) machinery exhibits inefficiency in recognizing specific bulky DNA adducts including Benzo[a]pyrene Diol-Epoxide (BPDE), a Benzo[a]pyrene metabolite. While sequence context is emerging as the leading factor linking the inadequate NER response to BPDE adducts, the precise structural attributes governing these disparities remain inadequately understood. We therefore combined the domains of molecular dynamics and machine learning to conduct a comprehensive assessment of helical distortion caused by BPDE-Guanine adducts in multiple gene contexts. Specifically, we implemented a dual approach involving a random forest classification-based analysis and subsequent feature selection to identify precise topological features that may distinguish adduct sites of variable repair capacity. Our models were trained using helical data extracted from duplexes representing both BPDE hotspot and nonhotspot sites within the TP53 gene, then applied to sites within TP53, cII, and lacZ genes.

Results

We show our optimized model consistently achieved exceptional performance, with accuracy, precision, and f1 scores exceeding 91%. Our feature selection approach uncovered that discernible variance in regional base pair rotation played a pivotal role in informing the decisions of our model. Notably, these disparities were highly conserved among TP53 and lacZ duplexes and appeared to be influenced by the regional GC content. As such, our findings suggest that there are indeed conserved topological features distinguishing hotspots and nonhotpot sites, highlighting regional GC content as a potential biomarker for mutation.

Availability and implementation

Code for comparing machine learning classifiers and evaluating their performance is available at https://github.com/jdavies24/ML-Classifier-Comparison, and code for analysing DNA structure with Curves+ and Canal using Random Forest is available at https://github.com/jdavies24/ML-classification-of-DNA-trajectories.

Stability Oracle: a structure-based graph-transformer framework for identifying stabilizing mutations

Engineering stabilized proteins is a fundamental challenge in the development of industrial and pharmaceutical biotechnologies. We present Stability Oracle: a structure-based graph-transformer framework that achieves SOTA performance on accurately identifying thermodynamically stabilizing mutations. Our framework introduces several innovations to overcome well-known challenges in data scarcity and bias, generalization, and computation time, such as: Thermodynamic Permutations for data augmentation, structural amino acid embeddings to model a mutation with a single structure, a protein structure-specific attention-bias mechanism that makes transformers a viable alternative to graph neural networks. We provide training/test splits that mitigate data leakage and ensure proper model evaluation. Furthermore, to examine our data engineering contributions, we fine-tune ESM2 representations (Prostata-IFML) and achieve SOTA for sequence-based models. Notably, Stability Oracle outperforms Prostata-IFML even though it was pretrained on 2000X less proteins and has 548X less parameters. Our framework establishes a path for fine-tuning structure-based transformers to virtually any phenotype, a necessary task for accelerating the development of protein-based biotechnologies.

Directed Evolution of Protoglobin Optimizes the Enzyme Electric Field

To unravel why computational design fails in creating viable enzymes, while directed evolution (DE) succeeds, our research delves into the laboratory evolution of protoglobin. DE has adapted this protein to efficiently catalyze carbene transfer reactions. We show that the previously proposed enhanced substrate access and binding alone cannot account for increased yields during DE. The 3D electric field in the entire active site is tracked through protein dynamics, clustered using the affinity propagation algorithm, and subjected to principal component analysis. This analysis reveals notable changes in the electric field with DE, where distinct field topologies influence transition state energetics and mechanism. A chemically meaningful field component emerges and takes the lead during DE and facilitates crossing the barrier to carbene transfer. Our findings underscore intrinsic electric field dynamic's influence on enzyme function, the ability of the field to switch mechanisms within the same protein, and the crucial role of the field in enzyme design.

Biosensor and machine learning-aided engineering of an amaryllidaceae enzyme

A major challenge to achieving industry-scale biomanufacturing of therapeutic alkaloids is the slow process of biocatalyst engineering. Amaryllidaceae alkaloids, such as the Alzheimer's medication galantamine, are complex plant secondary metabolites with recognized therapeutic value. Due to their difficult synthesis they are regularly sourced by extraction and purification from the low-yielding daffodil Narcissus pseudonarcissus. Here, we propose an efficient biosensor-machine learning technology stack for biocatalyst development, which we apply to engineer an Amaryllidaceae enzyme in Escherichia coli. Directed evolution is used to develop a highly sensitive (EC50 = 20 μM) and specific biosensor for the key Amaryllidaceae alkaloid branchpoint 4'-O-methylnorbelladine. A structure-based residual neural network (MutComputeX) is subsequently developed and used to generate activity-enriched variants of a plant methyltransferase, which are rapidly screened with the biosensor. Functional enzyme variants are identified that yield a 60% improvement in product titer, 2-fold higher catalytic activity, and 3-fold lower off-product regioisomer formation. A solved crystal structure elucidates the mechanism behind key beneficial mutations.

Exploring the impact of alignment media on RDC analysis of phosphorus-containing compounds: a molecular docking approach

Residual dipolar couplings (RDCs) are employed in NMR analysis when conventional methods, such as J-couplings and nuclear Overhauser effects (NOEs) fail. Low-energy (optimized) conformers are often used as input structures in RDC analysis programs. However, these low-energy structures do not necessarily resemble conformations found in anisotropic environments due to interactions with the alignment medium, especially if the analyte molecules are flexible. Considering interactions with alignment media in RDC analysis, we developed and evaluated a molecular docking-based approach to generate more accurate conformer ensembles for compounds in the presence of the poly-γ-benzyl-L-glutamate alignment medium. We designed chiral phosphorus-containing compounds that enabled us to utilize 31P NMR parameters for the stereochemical analysis. Using P3D/PALES software to evaluate diastereomer discrimination, we found that our conformer ensembles outperform moderately the standard, low-energy conformers in RDC analysis. To further improve our results, we (i) averaged the experimental values of the molecular docking-based conformers by applying the Boltzmann distribution and (ii) optimized the structures through normal mode relaxation, thereby enhancing the Pearson correlation factor R and even diastereomer discrimination in some cases. Nevertheless, we presume that significant differences between J-couplings in isotropic and in anisotropic environments may preclude RDC measurements for flexible molecules. Therefore, generating conformer ensembles based on molecular docking enhances RDC analysis for mildly flexible systems while flexible molecules may require applying more advanced approaches, in particular approaches including dynamical effects.

A comparative study on penetration mechanisms of drug-loaded carbon and boron nitride nanotubes through biological membranes by steered molecular dynamics simulations

Understanding the mechanism by which nanotubes penetrate cell membranes is challenging from multiple perspectives. As a drug delivery system, boron nitride nanotubes (BNNTs) have a similar structure to carbon nanotubes but with B-N bonds instead of C-C bonds. Through a remarkable series of direct and indirect observations within specific cells, these nanotubes are popularly attributed as superior penetrant into cell membrane. Suitable functional groups and polymers are often needed to enhance the biocompatibility and solubility of BNNTs and CNTs in biological media. In addition, to figure out the effect of functional groups, the nanostructures without functional groups were first examined together with their anticancer drugs (fluorouracil and letrozole). All partial charges of the drug and nanotube have been investigated through population analysis. After that, with a total of 40 simulations (MD and SMD simulations), various analytical techniques were employed to examine the interaction between drugs and nanotubes with POPE, which is a class of phospholipids existing in biological membrane, in aqueous media. Noteworthy among these techniques is the mean-squared displacement analysis to compare the diffusion rate of nanocarriers and the radial distribution function analysis, which was utilized to compare water concentrations surrounding nanotubes. Additionally, the stability of the drug within the nanotube was assessed through mass center distance analysis. The diffusion coefficients of the nanotube-membrane complex were compared against various chemical agents by employing mean squared displacement analysis. The findings of the study revealed that the tethering of tetra ethylene glycol results in the augmentation of the water molecules surrounding the nanotubes while simultaneously enhancing the durability of the drug being conveyed. Nonetheless, the addition of tetra ethylene glycol resulted in a reduction in the nanocarrier's diffusion coefficient.Communicated by Ramaswamy H. Sarma.

A Neural-Network-Optimized Hydrogen Peroxide Pairwise Additive Model for Classical Simulations

We have developed an all-atom pairwise additive model for hydrogen peroxide using an optimization procedure based on artificial neural networks (ANNs). The model is based on experimental molecular geometry and includes a dihedral potential that hinders the cis-type configuration and allows for crossing the trans one, defined between the planes that have the two oxygen atoms and each hydrogen. The model's parametrization is achieved by training simple ANNs to minimize a target function that measures the differences between various thermodynamic and transport properties and the corresponding experimental values. Finally, we evaluated a range of properties for the optimized model and its mixtures with SPC/E water, including bulk-liquid properties (density, thermal expansion coefficient, adiabatic compressibility, etc.) and properties of systems at equilibrium (vapor and liquid density, vapor pressure and composition, surface tension, etc.). Overall, we obtained good agreement with experimental data.

Interaction of Phospholipid, Cholesterol, Beta-Carotene, and Vitamin C Molecules in Liposome-Based Drug Delivery Systems: An In Silico Study

This paper investigates the interaction within a liposome-based drug delivery system in silico. Results confirmed that phospholipids, cholesterol, beta-carotene, and vitamin C in the liposome structures interact noncovalently. The formation of noncovalent interactions indicates that the liposomal structures from phospholipid molecules will not result in chemical changes to the drug or any molecules encapsulated within. Noncovalent interactions formed include (i) moderate-strength hydrogen bonds with interaction energies ranging from -73.6434 kJ·mol^-1 to -45.6734 kJ·mol^-1 and bond lengths ranging from 1.731 Å to 1.827 Å and (ii) van der Waals interactions (induced dipole-induced dipole and induced dipole-dipole interactions) with interaction energies ranging from -4.4735 kJ·mol^-1 to -1.5840 kJ·mol^-1 and bond lengths ranging from 3.192 Å to 3.742 Å. The studies for several phospholipids with short hydrocarbon chains show that changes in chain length have almost no effect on interaction energy, bond length, and partial atomic charge.

S-Adenosyl-l-Homocysteine Exhibits Potential Antiviral Activity Against Dengue Virus Serotype-3 (DENV-3) in Bangladesh: A Viroinformatics-Based Approach

Dengue outbreak is one of the concerning issues in Bangladesh due to the annual outbreak with the alarming number of death and infection. However, there is no effective antiviral drug available to treat dengue-infected patients. This study evaluated and screened antiviral drug candidates against dengue virus serotype 3 (DENV-3) through viroinformatics-based analyses. Since 2017, DENV-3 has been the predominant serotype in Bangladesh. We selected 3 non-structural proteins of DENV-3, named NS3, NS4A, and NS5, as antiviral targets. Protein modeling and validation were performed with VERIFY-3D, Ramachandran plotting, MolProbity, and PROCHECK. We found 4 drug-like compounds from DRUGBANK that can interact with these non-structural proteins of DENV-3. Then, the ADMET profile of these compounds was determined by admetSAR2, and molecular docking was performed with AutoDock, SWISSDOCK, PatchDock, and FireDock. Furthermore, they were subjected to molecular dynamics (MD) simulation study using the DESMOND module of MAESTRO academic version 2021-4 (force field OPLS_2005) to determine their solution's stability in a predefined body environment. Two drug-like compounds named Guanosine-5'-Triphosphate (DB04137) and S-adenosyl-l-homocysteine (DB01752) were found to have an effective binding with these 3 proteins (binding energy > 33.47 KJ/mole). We found NS5 protein was stable and equilibrated in a 100 ns simulation run along with a negligible (<3Å) root-mean-square fluctuation value. The root-mean-square deviation value of the S-adenosyl-l-homocysteine-NS5 complex was less than 3Å, indicating stable binding between them. The global binding energy of S-adenosyl-l-homocysteine with NS5 was -40.52 KJ/mole as ∆G. Moreover, these 2 compounds mentioned above are non-carcinogenic according to their ADMET (chemical absorption, distribution, metabolism, excretion, and toxicity) profile (in silico). These outcomes suggest the suitability of S-adenosyl-l-homocysteine as a potential drug candidate for dengue drug discovery research.

Is an Inductive Effect Explicit Account Required for Atomic Charges Aimed at Use within the Force Fields?

Polarization and inductive effects are the concepts that have been widely used in qualitative and even quantitative descriptions of experimentally observed properties in chemistry. The polarization effect has proven to be important in cases of biomolecular modeling though still the vast majority of molecular simulations use the classical non-polarizable force fields. In the last few decades, a lot of effort has been put into promoting the polarization effect and incorporating it into modern force fields and charge calculation methods. In contrast, the inductive effect has not attracted such attention and is effectively absent in both classic and modern force fields. Thus, a question is whether this difference corresponds to the difference in the physical significance of the effects and their explicit account, or is an artifact that should be corrected in the next generation of force fields. The significance of the electronic effects is studied in this paper through the prism of performance of specific models for atomic charge calculation that take into explicit account a nested set of effects: the formal charge, the nearest neighbors, the inductive effect, and finally the model, which takes into account all effects, which are possible to account for using atomic charges. The specific choice for the methods is the following: formal charges, MMFF94 bond charge increments, Dynamic Electronegativity Relaxation (DENR), and RESP. We propose a special scheme for the separate estimation of each particular effect contribution. By pairwise comparing the residual molecular electrostatic potential (MEP) errors of those charge models (aimed at best reproducing the quantum chemical reference MEP), we sequentially revealed how the account of each effect contributes to the better-quality MEP reproduction. The following relative importance of effects was estimated; thus, the natural hierarchy of the effects was established. First, the account of formal charges is of primordial importance. Second, the nearest neighbors account is the next in significance. Third, the explicit account of inductive effect in empirical charge calculation schemes was shown to significantly─both qualitatively and quantitatively─improve the quality of MEP reproduction. Fourth, the contribution of polarization is indirectly assessed. Surprisingly, it is of the order of magnitude of the inductive effect even for the molecular systems, for which it is anticipated to be more significant. Finally, the relative importance of anisotropic effects in neutral molecules was additionally reviewed.

31P NMR parameters may facilitate the stereochemical analysis of phosphorus-containing compounds

Conventional Nuclear Magnetic Resonance (NMR) analysis relies on H-H/C-H interactions. However, these interactions are sometimes insufficient for an accurate and precise NMR analysis. In this study, we show that ³¹P NMR parameters can provide critical structural insights into the stereochemistry of phosphorus-containing compounds. For this purpose, we prepared a set of model phosphorus-based proline derivatives, separated diastereoisomers, and determined their absolute configuration by single-crystal X-ray diffraction. After supplementing these results by electronic circular dichroism (ECD) spectroscopy, we combined experimental data and DFT calculations from our model compounds to perform a detailed conformational analysis, thereby determining their relative configuration. Overall, our findings establish an experimental paradigm for combining ³¹P NMR spectroscopy with other optical methods to facilitate the stereochemical analysis of phosphorus-containing compounds.

Out-of-the-box deep learning prediction of quantum-mechanical partial charges by graph representation and transfer learning

Accurate prediction of atomic partial charges with high-level quantum mechanics (QM) methods suffers from high computational cost. Numerous feature-engineered machine learning (ML)-based predictors with favorable computability and reliability have been developed as alternatives. However, extensive expertise effort was needed for feature engineering of atom chemical environment, which may consequently introduce domain bias. In this study, SuperAtomicCharge, a data-driven deep graph learning framework, was proposed to predict three important types of partial charges (i.e. RESP, DDEC4 and DDEC78) derived from high-level QM calculations based on the structures of molecules. SuperAtomicCharge was designed to simultaneously exploit the 2D and 3D structural information of molecules, which was proved to be an effective way to improve the prediction accuracy of the model. Moreover, a simple transfer learning strategy and a multitask learning strategy based on self-supervised descriptors were also employed to further improve the prediction accuracy of the proposed model. Compared with the latest baselines, including one GNN-based predictor and two ML-based predictors, SuperAtomicCharge showed better performance on all the three external test sets and had better usability and portability. Furthermore, the QM partial charges of new molecules predicted by SuperAtomicCharge can be efficiently used in drug design applications such as structure-based virtual screening, where the predicted RESP and DDEC4 charges of new molecules showed more robust scoring and screening power than the commonly used partial charges. Finally, two tools including an online server (http://cadd.zju.edu.cn/deepchargepredictor) and the source code command lines (https://github.com/zjujdj/SuperAtomicCharge) were developed for the easy access of the SuperAtomicCharge services.

A novel multi-objective metaheuristic algorithm for protein-peptide docking and benchmarking on the LEADS-PEP dataset

Protein-peptide interactions have attracted the attention of many drug discovery scientists due to their possible druggability features on most key biological activities such as regulating disease-related signaling pathways and enhancing the immune system's responses. Different studies have utilized some protein-peptide-specific docking algorithms/methods to predict protein-peptide interactions. However, the existing algorithms/methods suffer from two serious limitations which make them unsuitable for protein-peptide docking problems. First, it seems that the prevalent approaches require to be modified and remodeled for weighting the unbounded forces between a protein and a peptide. Second, they do not employ state-of-the-art search algorithms for detecting the 3D pose of a peptide relative to a protein. To address these restrictions, the present study aims to introduce a novel multi-objective algorithm, which first generates some potential 3D poses of a peptide, and then, improves them through its operators. The candidate solutions are further evaluated using Multi-Objective Pareto Front (MOPF) optimization concepts. To this end, van der Waals, electrostatic, solvation, and hydrogen bond energies between the atoms of a protein and designated peptide are computed. To evaluate the algorithm, it is first applied to the LEADS-PEP dataset containing 53 protein-peptide complexes with up to 53 rotatable branches/bonds and then compared with three popular/efficient algorithms. The obtained results indicate that the MOPF-based approaches which reduce the backbone RMSD between the original and predicted states, achieve significantly better results in terms of the success rate in predicting the near-native conditions. Besides, a comparison between the different types of search algorithms reveals that efficient ones like the multi-objective Trader/differential evolution algorithm can predict protein-peptide interactions better than the popular algorithms such as the multi-objective genetic/particle swarm optimization algorithms.

Optimized SQE atomic charges for peptides accessible via a web application

Background

Partial atomic charges find many applications in computational chemistry, chemoinformatics, bioinformatics, and nanoscience. Currently, frequently used methods for charge calculation are the Electronegativity Equalization Method (EEM), Charge Equilibration method (QEq), and Extended QEq (EQeq). They all are fast, even for large molecules, but require empirical parameters. However, even these advanced methods have limitations—e.g., their application for peptides, proteins, and other macromolecules is problematic. An empirical charge calculation method that is promising for peptides and other macromolecular systems is the Split-charge Equilibration method (SQE) and its extension SQE+q0. Unfortunately, only one parameter set is available for these methods, and their implementation is not easily accessible.

Results

In this article, we present for the first time an optimized guided minimization method (optGM) for the fast parameterization of empirical charge calculation methods and compare it with the currently available guided minimization (GDMIN) method. Then, we introduce a further extension to SQE, SQE+qp, adapted for peptide datasets, and compare it with the common approaches EEM, QEq EQeq, SQE, and SQE+q0. Finally, we integrate SQE and SQE+qp into the web application Atomic Charge Calculator II (ACC II), including several parameter sets.

Conclusion

The main contribution of the article is that it makes SQE methods with their parameters accessible to the users via the ACC II web application (https://acc2.ncbr.muni.cz) and also via a command-line application. Furthermore, our improvement, SQE+qp, provides an excellent solution for peptide datasets. Additionally, optGM provides comparable parameters to GDMIN in a markedly shorter time. Therefore, optGM allows us to perform parameterizations for charge calculation methods with more parameters (e.g., SQE and its extensions) using large datasets.

Graphic Abstract

In Silico and Electrochemical Studies for a ZnO-CuO-Based Immunosensor for Sensitive and Selective Detection of E. coli

Escherichia coli is a harmful Gram-negative bacterium commonly found in the gut of warm-blooded organisms and affects millions of people annually worldwide. In this study, we have synthesized a ZnO-CuO nanocomposite (NC) by a co-precipitation method and characterized the as-synthesized NC using FTIR spectroscopy, XRD, Raman spectroscopy, and FESEM techniques. To fabricate the immunosensor, the ZnO-CuO NC composite was screen-printed on gold-plated electrodes followed by physisorption of the anti-LPS E. coli antibody. The biosensor was optimized for higher specificity and sensitivity. The immunosensor exhibited a high sensitivity (11.04 μA CFU mL-1) with a low detection limit of 2 CFU mL-1 with a redox couple. The improved performance of the immunosensor is attributed to the synergistic effect of the NC and the antilipopolysaccharide antibody against E. coli. The selectivity studies were also carried out with Staphylococcus aureus to assess the specificity of the immunosensor. Testing in milk samples was done by spiking the milk samples with different concentrations of E. coli to check the potential of this immunosensor. We further checked the affinity between ZnO-CuO NC with E. coli LPS and the anti-LPS antibody using molecular docking studies. Atomic charge computation and interaction analyses were performed to support our hypothesis. Our results discern that there is a strong correlation between molecular docking studies and electrochemical characterization. The interaction analysis further displays the strong affinity between the antibody-LPS complex when immobilized with a nanoparticle composite (ZnO-CuO).

DeepChargePredictor: A web server for predicting QM-based atomic charges via state-of-the-art machine-learning algorithms

SUMMARY

High-level quantum mechanics (QM) methods are no doubt the most reliable approaches for the prediction of atomic charges, but it usually needs very large computational resources, which apparently hinders the use of high-quality atomic charges in large-scale molecular modeling, such as high-throughput virtual screening. To solve this problem, several algorithms based on machine-learning (ML) have been developed to fit high-level QM atomic charges. Here, we proposed DeepChargePredictor, a web server that is able to generate the high-level QM atomic charges for small molecules based on two state-of-the-art ML algorithms developed in our group, namely AtomPathDescriptor and DeepAtomicCharge. These two algorithms were seamlessly integrated into the platform with the capability to predict three kinds of charges (i.e., RESP, AM1-BCC and DDEC) widely used in structure-based drug design. Moreover, we have comprehensively evaluated the performance of these charges generated by DeepChargePredictor for large-scale drug design applications, such as end-point binding free energy calculations and virtual screening, which all show reliable or even better performance compared with the baseline methods.

AVAILABILITY AND IMPLEMENTATION

DeepChargePredictor server is accessible at http://cadd.zju.edu.cn/deepchargepredictor/.

Seven confluence principles: a case study of standardized statistical analysis for 26 methods that assign net atomic charges in molecules

This article studies two kinds of information extracted from statistical correlations between methods for assigning net atomic charges (NACs) in molecules. First, relative charge transfer magnitudes are quantified by performing instant least squares fitting (ILSF) on the NACs reported by Cho et al. (ChemPhysChem, 2020, 21, 688-696) across 26 methods applied to ∼2000 molecules. The Hirshfeld and Voronoi deformation density (VDD) methods had the smallest charge transfer magnitudes, while the quantum theory of atoms in molecules (QTAIM) method had the largest charge transfer magnitude. Methods optimized to reproduce the molecular dipole moment (e.g., ACP, ADCH, CM5) have smaller charge transfer magnitudes than methods optimized to reproduce the molecular electrostatic potential (e.g., CHELPG, HLY, MK, RESP). Several methods had charge transfer magnitudes even larger than the electrostatic potential fitting group. Second, confluence between different charge assignment methods is quantified to identify which charge assignment method produces the best NAC values for predicting via linear correlations the results of 20 charge assignment methods having a complete basis set limit across the dataset of ∼2000 molecules. The DDEC6 NACs were the best such predictor of the entire dataset. Seven confluence principles are introduced explaining why confluent quantitative descriptors offer predictive advantages for modeling a broad range of physical properties and target applications. These confluence principles can be applied in various fields of scientific inquiry. A theory is derived showing confluence is better revealed by standardized statistical analysis (e.g., principal components analysis of the correlation matrix and standardized reversible linear regression) than by unstandardized statistical analysis. These confluence principles were used together with other key principles and the scientific method to make assigning atom-in-material properties non-arbitrary. The N@C60 system provides an unambiguous and non-arbitrary falsifiable test of atomic population analysis methods. The HLY, ISA, MK, and RESP methods failed for this material.

High-performance macromolecular data delivery and visualization for the web

Biomacromolecular structural data make up a vital and crucial scientific resource that has grown not only in terms of its amount but also in its size and complexity. Furthermore, these data are accompanied by large and increasing amounts of experimental data. Additionally, the macromolecular data are enriched with value-added annotations describing their biological, physicochemical and structural properties. Today, the scientific community requires fast and fully interactive web visualization to exploit this complex structural information. This article provides a survey of the available cutting-edge web services that address this challenge. Specifically, it focuses on data-delivery problems, discusses the visualization of a single structure, including experimental data and annotations, and concludes with a focus on the results of molecular-dynamics simulations and the visualization of structural ensembles.