Structural Descriptors Research Articles

Machine Learning Integrating Protein Structure, Sequence, and Dynamics to Predict the Enzyme Activity of Bovine Enterokinase Variants.

Despite recent advances in computational protein science, the dynamic behavior of proteins, which directly governs their biological activity, cannot be gleaned from sequence information alone. To overcome this challenge, we propose a framework that integrates the peptide sequence, protein structure, and protein dynamics descriptors into machine learning algorithms to enhance their predictive capabilities and achieve improved prediction of the protein variant function. The resulting machine learning pipeline integrates traditional sequence and structure information with molecular dynamics simulation data to predict the effects of multiple point mutations on the fold improvement of the activity of bovine enterokinase variants. This study highlights how the combination of structural and dynamic data can provide predictive insights into protein functionality and address protein engineering challenges in industrial contexts.

Synthesis, characterisation and topological indices approach of hydrazone derivatives of disubstituted-2,6-diaryl piperidin-4-one, catalysed by CdO-SnO2 nanocatalyst

The nanocatalyst CdO-SnO2 was effectively prepared using the co-precipitation method. Hydrazone derivatives of disubstituted-2,6-diaryl piperidin-4-one were synthesised using the nanocatalyst. All of the synthesised compounds were characterised using their IR and NMR spectra. The topological data from the molecular graphs was extracted using quantifiers from graph theory. These quantifiers, in turn, produced useful functions that made connections between different material attributes. These links were made possible by structural descriptors or, more frequently, graph invariants. Topological indices, which provide numerical functions that represent the molecular structure, were significant among the structural descriptors. Quantitative structure activity relations (QSAR) and quantitative structure property relations (QSPR) were made possible thanks to these indices. The calculated values were successfully used to determine the biological and chemical characteristics of the synthesised compounds.

Decoding crystallization behavior of aluminoborosilicate glasses: From structural descriptors to Quantitative Structure – Property Relationship (QSPR) based predictive models

Successful decoding of structural descriptors controlling the crystallization in multicomponent functional glasses can pave the way for the transition from the trial-and-error approach and empirical modeling for glass/glass-ceramic composition design toward more rational and scientifically rigorous Quantitative Structure-Property Relationship (QSPR) based models. However, due to the compositional and structural complexity of multicomponent glasses and the longer time and length scales associated with nucleation, the development and validation of QSPR models are still in it's infancy. The work presented in the article is an attempt to leap forward in this pursuit by combining the strengths of experimental and computational materials science to decode the chemo-structural drivers that promote or suppress nucleation and crystal growth in alkali/alkaline-earth aluminoborosilicate glasses leading to the development of a QSPR-based model (powered by MD simulations). The results reveal the following two descriptors that govern the nucleation and crystallization of a particular aluminosilicate phase in the functional glasses: (1) degree of mixing between the SiO4 and AlO4 units, i.e., Si–O–Al linkages, and (2) difference/similarity between the short-to-intermediate range ordering in the glass structure to that of the structure of corresponding crystalline phase. Based on the established composition–structure–crystallization behavior relationships, a cluster analysis based QSPR model has been developed (and tested) to predict the propensity of nepheline (and anorthite) crystallization in the investigated glasses. The model has been tested on several compositions from the present and previous studies and has successfully predicted the crystallization propensity of all glass compositions, even in cases where previous empirical and semi-empirical models were unsuccessful.

Application of machine learning-based read-across structure-property relationship (RASPR) as a new tool for predictive modelling: Prediction of power conversion efficiency (PCE) for selected classes of organic dyes in dye-sensitized solar cells (DSSCs).

The application of various in-silico-based approaches for the prediction of various properties of materials has been an effective alternative to experimental methods. Recently, the concepts of Quantitative structure-property relationship (QSPR) and read-across (RA) methods were merged to develop a new emerging chemoinformatic tool: read-across structure-property relationship (RASPR). The RASPR method can be applicable to both large and small datasets as it uses various similarity and error-based measures. It has also been observed that RASPR models tend to have an increased external predictivity compared to the corresponding QSPR models. In this study, we have modeled the power conversion efficiency (PCE) of organic dyes used in dye-sensitized solar cells (DSSCs) by using the quantitative RASPR (q-RASPR) method. We have used relatively larger classes of organic dyes-Phenothiazines (n=207), Porphyrins (n=281), and Triphenylamines (n=229) for the modelling purpose. We have divided each of the datasets into training and test sets in 3 different combinations, and with the training sets we have developed three different QSPR models with structural and physicochemical descriptors and validated them with the corresponding test sets. These corresponding modeled descriptors were used to calculate the RASPR descriptors using a Java-based tool RASAR Descriptor Calculator v2.0 (https://sites.google.com/jadavpuruniversity.in/dtc-lab-software/home), and then data fusion was performed by pooling the previously selected structural and physicochemical descriptors with the calculated RASPR descriptors. Further feature selection algorithm was employed to develop the final RASPR PLS models. Here, we also developed different machine learning (ML) models with the descriptors selected in the QSPR PLS and RASPR PLS models, and it was found that models with RASPR descriptors superseded in external predictivity the models with only structural and physicochemical descriptors: RMSEP reduced for phenothiazines from 1.16-1.25 to 1.07-1.18, for porphyrins from 1.60-1.79 to 1.45-1.53, for triphenylamines from 1.27-1.54 to 1.20-1.47.

Computation of Structural Descriptors of Pyrene Cored Dendrimers through Quotient Graph Approach and Its Graph Entropy Measures

Dendrimers are highly defined hyperbranched artificial macromolecules, synthesised by convergent or divergent approach with specific applications in various fields. Dendrimers can be represented as graph models, from which a quantitative description can be drawn in relation with their structural properties. The distance-based and the degree-based descriptors have great importance and huge applications in structural chemistry. These indices together with entropy measures are found to be more effective and have found application in scientific fields. The idea of graph entropy is to characterise the complexity of graphs. The use of these graph invariants in quantitative structure property relationship and quantitative structure activity relationship studies has become of major interest in recent years. In this paper, the distance-based molecular descriptors of pyrene cored dendrimers are studied applying the technique of converting original graph into quotient graphs using Θ∗-classes. It is to be noted that, since the pyrene cored dendrimer, Gn is not a partial cube, usual cut method is not applicable. Further, various degree-based descriptors and their corresponding graph entropies of the pyrene cored dendrimers are also studied. Based on the obtained results, a comparative analysis as well as a regression analysis was carried out.

Identification of SARS-CoV-2 Main Protease Inhibitors Using Chemical Similarity Analysis Combined with Machine Learning.

SARS-CoV-2 Main Protease (Mpro) is an enzyme that cleaves viral polyproteins translated from the viral genome, which is critical for viral replication. Mpro is a target for anti-SARS-CoV-2 drug development. Herein, we performed a large-scale virtual screening by comparing multiple structural descriptors of reference molecules with reported anti-coronavirus activity against a library with >17 million compounds. Further filtering, performed by applying two machine learning algorithms, identified eighteen computational hits as anti-SARS-CoV-2 compounds with high structural diversity and drug-like properties. The activities of twelve compounds on Mpro's enzymatic activity were evaluated by fluorescence resonance energy transfer (FRET) assays. Compound 13 (ZINC13878776) significantly inhibited SARS-CoV-2 Mpro activity and was employed as a reference for an experimentally hit expansion. The structural analogues 13a (ZINC4248385), 13b (ZNC13523222), and 13c (ZINC4248365) were tested as Mpro inhibitors, reducing the enzymatic activity of recombinant Mpro with potency as follows: 13c > 13 > 13b > 13a. Then, their anti-SARS-CoV-2 activities were evaluated in plaque reduction assays using Vero CCL81 cells. Subtoxic concentrations of compounds 13a, 13c, and 13b displayed in vitro antiviral activity with IC50 in the mid micromolar range. Compounds 13a-c could become lead compounds for the development of new Mpro inhibitors with improved activity against anti-SARS-CoV-2.

On the Entire Harmonic Index and Entire Harmonic Polynomial of Graphs

A topological descriptor is a numerical parameter that describes a chemical structure using the related molecular graph. Topological descriptors have significance in mathematical chemistry, particularly for studying QSPR and QSAR. In addition, if a topological descriptor has a reciprocal link with a molecular attribute, it is referred to as a topological index. The use of topological indices can help to examine the physicochemical features of chemical compounds because they encode certain attributes of a molecule. The Randić index is a molecular structure descriptor that has several applications in chemistry and medicine. In this paper, we introduce a new version of the Randić index to the inclusion of the intermolecular forces between bonds with atoms, referred to as an entire Harmonic index (EHI), and we present the entire Harmonic polynomial (EHP) of a graph. Specific formulas have been obtained for certain graph classes, and graph operations have been obtained. Bounds and some important results have been found. Furthermore, we demonstrate that the correlation coefficients for the new index lie between 0.909 and 1. In the context of enthalpy of formation and π-electronic energy, the acquired values are significantly higher than those observed for the Harmonic index and the Randić index.

Topological Data Analysis Combined with High-Throughput Computational Screening of Hydrophobic Metal–Organic Frameworks: Application to the Adsorptive Separation of C3 Components

The shape and topology of pores have significant impacts on the gas storage properties of nanoporous materials. Metal–organic frameworks (MOFs) are ideal materials with which to tailor to the needs of specific applications, due to properties such as their tunable structure and high specific surface area. It is, therefore, particularly important to develop descriptors that accurately identify the topological features of MOF pores. In this work, a topological data analysis method was used to develop a topological descriptor, based on the pore topology, which was combined with the Extreme Gradient Boosting (XGBoost) algorithm to predict the adsorption performance of MOFs for methane/ethane/propane. The final results show that this descriptor can accurately predict the performance of MOFs, and the introduction of the topological descriptor also significantly improves the accuracy of the model, resulting in an increase of up to 17.55% in the R2 value of the model and a decrease of up to 46.1% in the RMSE, compared to commonly used models that are based on the structural descriptor. The results of this study contribute to a deeper understanding of the relationship between the performance and structure of MOFs and provide useful guidelines and strategies for the design of high-performance separation materials.

Machine Learning for Quantitative Structural Information from Infrared Spectra: The Case of Palladium Hydride.

Infrared spectroscopy (IR) is a widely used technique enabling to identify specific functional groups in the molecule of interest based on their characteristic vibrational modes or the presence of a specific adsorption site based on the characteristic vibrational mode of an adsorbed probe molecule. The interpretation of an IR spectrum is generally carried out within a fingerprint paradigm by comparing the observed spectral features with the features of known references or theoretical calculations. This work demonstrates a method for extracting quantitative structural information beyond this approach by application of machine learning (ML) algorithms. Taking palladium hydride formation as an example, Pd-H pressure-composition isotherms are reconstructed using IR data collected in situ in diffuse reflectance using CO molecule as a probe. To the best of the knowledge, this is the first example of the determination of continuous structural descriptors (such as interatomic distance and stoichiometric coefficient) from the fine structure of vibrational spectra, which opens new possibilities of using IR spectra for structural analysis.

Predictive potential of K‐Banhatti and Zagreb type molecular descriptors in structure–property relationship analysis of some novel drug molecules

AbstractTopological indices (TIs) can be used to forecast molecules' biological activity, physicochemical features, and toxicity. The use of topological indices and regression analysis can help us better understand drug behavior and contribute to the development of tailored drugs. This study investigates the predictive potential and efficacy of K‐Banhatti and Zagreb type degree‐based topological indices in quantitative structure–property relationship (QSPR) analysis of a comprehensive set of medications used for diabetes type‐I and type‐II disease. The K‐Banhatti and Zagreb type degree‐based topological indices are computed for 14 anti‐diabetes drug molecules using edge/vertex partitioning techniques. By leveraging these topological indices, QSPR regression models are developed to predict the physicochemical properties of the understudy drugs. The results show that the values of these topological indices are highly correlated with certain physicochemical properties of the anti‐diabetes drugs. Furthermore, the comparative analysis revealed that, for all the considered properties except enthalpy of vaporization, Zagreb type indices outperform K‐Banhatti indices with high predictive ability. Hence, it can be concluded that the Zagreb type indices are the best alternatives to theoretically predict the properties of anti‐diabetes drugs. This theoretical analysis can help chemists in their right choice of the topological indices to theoretically predict the properties of anti‐diabetes drugs without going into laborious experimentation.

Structural descriptor and surrogate modeling for design of biodegradable scaffolds

Biodegradable scaffolds are important to regenerative medicine in that they provide an amicable environment for tissue regrowth. However, establishing structure-property (SP) relationships for scaffold design is challenging due to the complexity of the three-dimensional porous scaffold geometry. The complexity requires high-dimensional geometric descriptors. The training of such a SP surrogate model will need a large amount of experimental or simulation data. In this work, a schema of constructing SP relationship surrogates is developed to predict the degraded mechanical properties from the initial scaffold geometry. A new structure descriptor, the extended surfacelet transform (EST), is proposed to capture important details of pores associated with the degradation of scaffolds. The efficiency is further enhanced with principal component analysis to reduce the high-dimensional EST data into a low-dimensional representation. The schema also includes a kinetic Monte Carlo biodegradation model to simulate the biodegradation of polymer scaffolds and to generate the training data for the formation of SP relationships. The schema is demonstrated with the design of polycaprolactone biodegradable scaffolds by connecting the initial scaffold geometry to the degraded compressive modulus.

Structural descriptors evaluation for MoTa mechanical properties prediction with machine learning

Considering all possible crystal structures is essential in computer simulations of alloy properties, but using density functional theory (DFT) is computationally impractical. To address this, four structural descriptors were evaluated using machine learning (ML) models to predict formation energy, elasticity and hardness of MoTa alloys. A total of 612 configurations were generated by the Clusters Approach to Statistical Mechanics software and their corresponding material properties were calculated by DFT. As input features of ML models, the CORR and SOAP performed best (R 2 > 0.90, some up to 0.99), followed by Atomic-centred Symmetry Functions, while Coulomb matrix performed worst. Furthermore, SOAP shows excellent performance in extrapolation for larger supercell structures of the MoTa alloy system and transfer learning for the MoNb alloy system.

Universal electronic descriptors for optimizing hydrogen evolution in transition metal-doped MXenes

The integration of transition metal (TM) atoms into two-dimensional (2D) transition metal carbides and nitrides (MXenes) has been identified as a promising strategy for enhancing hydrogen evolution reaction (HER) performance. However, the vast combinatorial space presents challenges for rapid catalyst screening. In response, high-throughput calculations were conducted on Ti2CO2 and Zr2CO2 doped with a wide array of TM single atoms. The local structural and corresponding electronic structural changes were analyzed, with an emphasis on their implications for HER performance. Furthermore, universal electronic descriptors were developed using the Sure Independence Screening and Sparsifying Operator (SISSO) method, harmonizing the Gibbs free energy of hydrogen adsorption (ΔGH*) trends across different MXenes substrates doped with TM atoms. These descriptors enabled the prediction of Cr, Fe, Ru, Pd, Os, and Ir as effective dopants for optimizing the ΔGH* of Hf2CO2, resulting in a reduction of ΔGH* from 1.082 eV to within ±0.2 eV. Our work not only highlights the potential of these TM dopants in significantly enhancing the catalytic activity of MXenes but also underscores the value of universal electronic structure descriptors in rapidly identifying and developing high-performance HER catalysts.

Deciphering the controlling factors for phase transitions in zeolitic imidazolate frameworks.

Zeolitic imidazolate frameworks (ZIFs) feature complex phase transitions, including polymorphism, melting, vitrification, and polyamorphism. Experimentally probing their structural evolution during transitions involving amorphous phases is a significant challenge, especially at the medium-range length scale. To overcome this challenge, here we first train a deep learning-based force field to identify the structural characteristics of both crystalline and non-crystalline ZIF phases. This allows us to reproduce the structural evolution trend during the melting of crystals and formation of ZIF glasses at various length scales with an accuracy comparable to that of ab initio molecular dynamics, yet at a much lower computational cost. Based on this approach, we propose a new structural descriptor, namely, the ring orientation index, to capture the propensity for crystallization of ZIF-4 (Zn(Im)2, Im=C3H3N2-) glasses, as well as for the formation of ZIF-zni (Zn(Im)2) out of the high-density amorphous phase. This crystal formation process is a result of the reorientation of imidazole rings by sacrificing the order of the structure around the zinc-centered tetrahedra. The outcomes of this work are useful for studying phase transitions in other metal-organic frameworks (MOFs) and may thus guide the development of MOF glasses.

Geometric approach to vertex‐degree‐based topological indices–Elliptic Sombor index, theory and application

AbstractA novel geometric method is proposed for constructing vertex‐degree‐based molecular structure descriptors (topological indices). The model is based on an ellipse whose focal points represent the degrees of a pair of adjacent vertices. This approach enables a geometric interpretation of several previously known topological indices, and lead to design of a few new. The area of the ellipse induces a vertex‐degree‐based topological index of remarkable simplicity, which we call elliptic Sombor index(). The main mathematical properties of are established, especially its relations to other, earlier known, indices. Then the applicative potential of is analyzed.

Machine Learning-Based Virtual Screening and Identification of the Fourth-Generation EGFR Inhibitors.

Epidermal growth factor receptor (EGFR) plays a pivotal regulatory role in treating patients with advanced nonsmall cell lung cancer (NSCLC). Following the emergence of the EGFR tertiary CIS C797S mutation, all types of inhibitors lose their inhibitory activity, necessitating the urgent development of new inhibitors. Computer systems employ machine learning methods to process substantial volumes of data and construct models that enable more accurate predictions of the outcomes of new inputs. The purpose of this article is to uncover innovative fourth-generation epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) with the aid of machine learning techniques. The paper's data set was high-dimensional and sparse, encompassing both structured and unstructured descriptors. To address this considerable challenge, we introduced a fusion framework to select critical molecule descriptors by integrating the full quadratic effect model and the Lasso model. Based on structural descriptors obtained from the full quadratic effect model, we conceived and synthesized a variety of small-molecule inhibitors. These inhibitors demonstrated potent inhibitory effects on the two mutated kinases L858R/T790M/C797S and Del19/T790M/C797S. Moreover, we applied our model to virtual screening, successfully identifying four hit compounds. We have evaluated these hit ADME characteristics and look forward to conducting activity evaluations on them in the future to discover a new generation of EGFR-TKI.

In silico evaluation of the role of Fab glycosylation in cetuximab antibody dynamics.

N-glycosylation is a post-translational modification that is highly important for the development of monoclonal antibodies (mAbs), as it regulates their biological activity, particularly in terms of immune effector functions. While typically added at the Fc level, approximately 15-25% of circulating antibodies exhibit glycosylation in the Fab domains as well. To the best of our knowledge, cetuximab (Erbitux®) is the only therapeutic antibody presenting Fab glycosylation approved world-wide targeting the epidermal growth factor receptor for the treatment of metastatic-colorectal and head and neck cancers. Additionally, it can trigger antibody-dependent cell cytotoxicity (ADCC), a response that typically is influenced by N-glycosylation at Fc level. However, the role of Fab glycosylation in cetuximab remains poorly understood. Hence, this study aims to investigate the structural role of Fab glycosylation on the conformational behavior of cetuximab. The study was performed in silico via accelerated molecular dynamics simulations. The commercial cetuximab was compared to its form without Fab glycosylation and structural descriptors were evaluated to establish conformational differences. The results clearly show a correlation between the Fab glycosylation and structural descriptors that may modulate the conformational freedom of the antibody, potentially affecting Fc effector functions, and suggesting a negative role of Fab glycosylation on the interaction with FcγRIIIa. Fab glycosylation of cetuximab is the most critical challenge for biosimilar development, but the differences highlighted in this work with respect to its aglycosylated form can improve the knowledge and represent also a great opportunity to develop novel strategies of biotherapeutics.

Blind Quality Evaluator of Light Field Images by Group-Based Representations and Multiple Plane-Oriented Perceptual Characteristics

Due to the emergency of multi-view cameras and commercial Light Field (LF) cameras, the demand of high-performance LF quality evaluator is of great significance for guiding LF acquisition, processing and application and further promoting the visual perceived quality of LF visualizations. However, LF Images (LFIs), as high-dimensional data, suffer from various quality degradations not only in the spatial domain but also in the angular domain. Therefore, it is of great challenge to predict LF quality accurately. An effective LF evaluator should be able to represent these heterogeneous artifacts. In this paper, we provide a novel No-Reference LF Quality Assessment Evaluator (NR LF-QAE) to tackle this problem. Firstly, to measure angular consistency among viewports, we utilize group-based representations to character information similarity of aligned view stacks. Secondly, to better describe the texture information of LFIs, unifying spatial-angular texture statistic measurement is performed via Local Binary Patterns from Three Orthogonal Planes (LBP-TOP). Thirdly, we design 3D Log-Gabor filters to extract LF global structure information in Sub-Aperture Images (SAIs) as spatial feature characterizations and 2D Log-Gabor filters are adopted to characterize ray direction/depth information in Epipolar Plane Images (EPIs) as angular feature characterizations. By comprehensive LF information analyses in angular consistency and spatial-angular feature extraction with texture and structure descriptors, experimental results demonstrate the superiority of the proposed NR LF-QAE over the state-of-the-art comparative models in predicting the quality of LFIs on three available benchmark databases. The code will be released at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/zerosola/NR-LF-QAE</uri> .

Structural descriptors and information extraction from X-ray emission spectra: aqueous sulfuric acid.

Machine learning can reveal new insights into X-ray spectroscopy of liquids when the local atomistic environment is presented to the model in a suitable way. Many unique structural descriptor families have been developed for this purpose. We benchmark the performance of six different descriptor families using a computational data set of 24 200 sulfur Kβ X-ray emission spectra of aqueous sulfuric acid simulated at six different concentrations. We train a feed-forward neural network to predict the spectra from the corresponding descriptor vectors and find that the local many-body tensor representation, smooth overlap of atomic positions and atom-centered symmetry functions excel in this comparison. We found a similar hierarchy when applying the emulator-based component analysis to identify and separate the spectrally relevant structural characteristics from the irrelevant ones. In this case, the spectra were dominantly dependent on the concentration of the system, whereas adding the second most significant degree of freedom in the decomposition allowed for distinction of the protonation state of the acid molecule.

Sex differences in the associations between right heart structure and peak exercise capacity parameters in amateur cyclists.

Introduction: Right heart changes and their association with exercise capacity, including sex differences, are still being investigated. We analysed right heart structure and its relationship with exercise capacity parameters in amateur cyclists. Materials and methods: A cross-sectional study involving 215 consecutive adult amateur cyclists, who underwent resting transthoracic echocardiography and a cardiopulmonary exercise test (CPET) to exhaustion was performed. Results: The median age of participants was 29 years (IQR 24-37), 71% of them were men. The mean training time was 6h/week, and 90% participated in vigorous or moderate physical activity. Men had larger right ventricular diameters (basal - RVD1, mid-cavity - RVD2 and longitudinal - RVD3) compared to women (40.9 vs. 37.6mm, p = 0.0005, 28.7 vs. 26.3mm, p = 0.03, 92.2 vs. 81.9mm, p < 0.0001). Indexing for body surface area revealed comparable right atrial volume (RAVI) between sexes (24.1 vs. 22.7mL/m2). Men achieved higher peak exercise capacity parameters [O2 pulse, oxygen consumption (VO2) and workload] in CPET. Multivariate linear regression models revealed a positive association between peak VO2, workload and O2 pulse with RAVI in women but not with RVD1 or RVD3. Conversely, these parameters showed a positive association with RVD3 and RVD1 but not with RAVI in men. Conclusion: Sex differences exist in the relationship between right heart structural parameters and peak exercise capacity descriptors in amateur cyclists. Better exercise capacity during CPET to exhaustion is associated with greater RAVI in women but a greater RVD1 and RVD3 in men. These findings suggest different mechanisms of right heart adaptation to training in men and women.