Instantaneous generation of protein hydration properties from static structures

In strained monoatomic chains with Lennard-Jones interactions, we revealed a stable static non-homogeneous structure appearing as a result of a certain phase transition. Positions of individual particles in this structure form an exact arithmetic progression whose difference depends on the value of the strain. For N-particle chain, this structure is characterized by one long and N-1 short interatomic distances (bonds). In the vicinity of the static structure, we found discrete breathers of new type which essentially differ from the traditional breathers in the form of Sievers-Takeno and Page modes. It is well known that these modes possess some staggered structures and demonstrate exponential decay of the particle amplitudes from the core to their tails. In contrast to such properties, our breathers are characterised by smooth decay and amplitudes of the particles form approximately a decreasing arithmetic progression. Core of these breathers is located on two particles with long bond in static structure. Our breathers demonstrate soft type of nonlinearity (the frequency decreases with increasing of amplitudes) and they are stable dynamical objects for amplitudes up to 20%-30% of interparticle distance of the strained equidistant chain. For infinitely small amplitudes these breathers tend to the above described static non-homogeneous structure. We studied dependence of their properties on amplitude, strain and the number of particles in the chain. There exist a reason to suppose that the above static and dynamical structures can exist in real monoatomic chains consisting of carbon, boron, and other atoms.

STATIC STRUCTURE, DYNAMIC FORM: AN ANALYSIS OF ELLIOTT CARTER’S CONCERTO FOR ORCHESTRA KLAAS COULEMBIER INTRODUCTION LLIOTT CARTER’S CONCERTO FOR ORCHESTRA is one of his most intriguing, complex, and fascinating compositions. It is a tour de force in the organization and arrangement of different musical materials, in the domains of both pitch and rhythm. Several authors have expressed their admiration for the composition, while acknowledging that it is very difficult to penetrate. In 1989, David Harvey opened his chapter on Carter’s Concerto for Orchestra with the following statement: The Concerto for Orchestra is Carter’s richest and most complex work to date in every respect. A complete account of its material, techniques, and their realisation in the textures of the work is beyond the scope of the present study; indeed, it may be doubted that such a project is at all feasible, given the size of the work, the density of the orchestral writing, the richness of the harmonic elaborations generated by Carter’s intervallic techniques of composition , and the limitations of analysis at the present time.1 E 98 Perspectives of New Music That this work is attractive to analysts is beyond question; the trepidation with which they have approached is, however, not only the result of its multi-layered musical surface, but also, no doubt, stems from the startling number of sketches Carter generated in producing it, conveying the impression that the construction of the composition may be even more puzzling than its form. Nevertheless, to gain a deeper insight into the workings of this music, the sketches appear to be as necessary as they are daunting. Jonathan Bernard concluded his 1983 article in Music Analysis with the remark that it would be impossible to make a fully comprehensive analysis of this composition. Dealing with the huge expanses of Carter’s scores . . . is still not easy. To retrace the steps of a composer who produces thousands of pages of sketches and works for thousands of hours in the course of writing a piece is likely to be a formidable undertaking, to say the least. . . . The prospect of reading, much less writing, a so-called “complete analysis” carried out according to the methods presented here is truly fearsome to contemplate. Eventually, perhaps, someone will have a bright idea that will make everything seem much simpler. Until then we can only have faith in the music, continue to analyse, and hope for the best.2 Rather than try to be that person with the bright idea, I want to contribute to the understanding of this composition by focusing on its overall temporal and dramatic organization. Therefore, in dealing with the more than 3000 pages of sketches that I studied during a weeklong stay at the Paul Sacher Foundation, I have kept my focus here exclusively on rhythmic sketches and temporal calculations. In that respect the following analysis complements existing literature in which pitch organization has often been the focal point. Despite the dependence on sketches, this analysis is not aimed at a mere reconstruction of the compositional process.3 By revealing the intricate relation between the rigid and static background structures and their more supple and subtle surface manifestations, this analysis tries to show which strategies and methods Carter applied to achieve such a compelling dramatic and dynamic musical discourse. THE CONCERTO FOR ORCHESTRA IN LITERATURE: AN OVERVIEW Apart from a chapter in David Harvey’s dissertation,4 there are no exhaustive, start-to-finish analyses of the Concerto for Orchestra. Static Structure, Dynamic Form 99 David Schiff gives a comprehensible overview of the composition in The Music of Elliott Carter.5 Jonathan Bernard has returned to the composition on several occasions, dealing with pitch structures in his article on “spatial sets,”6 or with the relationship between the literary source of the composition in “Poem as Non-Verbal Text.”7 Several publications of the Paul Sacher Foundation also include descriptions of the composition’s elaborate sketch resources available in their collection.8 Scholarly literature on Carter’s music in general has regained new energy in the last decade, particularly since the composer’s centennial celebration in 2008. Recent publications often consolidate important studies (such as Jonathan Bernard...

The emergence of deep learning, particularly AlphaFold, has revolutionized static protein structure prediction, marking a transformative milestone in structural biology. However, protein function is not solely determined by static three-dimensional structures but is fundamentally governed by dynamic transitions between multiple conformational states. This shift from static to multi-state representations is crucial for understanding the mechanistic basis of protein function and regulation. This review outlines the fundamental concepts of protein dynamic conformations, surveys recent computational advances in modeling these dynamics in the post-AlphaFold era, and highlights key challenges, including data limitations, methodological constraints, and evaluation metrics. We also discuss potential strategies to address these challenges and explore future research directions to deepen our understanding of protein dynamics and their functional implications. This work aims to provide insights and perspectives to facilitate the ongoing development of protein conformation studies in the era of artificial intelligence-driven structural biology.

Dynamic hydrogen-bond networks provide proteins with structural plasticity required to translate signals such as ligand binding into a cellular response or to transport ions and larger solutes across membranes and, thus, are of central interest to understand protein reaction mechanisms. Here, we present C-Graphs, an efficient tool with graphical user interface that analyzes data sets of static protein structures or of independent numerical simulations to identify conserved, vs unique, hydrogen bonds and hydrogen-bond networks. For static structures, which may belong to the same protein or to proteins with different sequences, C-Graphs uses a clustering algorithm to identify sites of the hydrogen-bond network where waters are conserved among the structures. Using C-Graphs, we identify an internal protein-water hydrogen-bond network common to static structures of visual rhodopsins and adenosine A2A G protein-coupled receptors (GPCRs). Molecular dynamics simulations of a visual rhodopsin indicate that the conserved hydrogen-bond network from static structure can recruit dynamic hydrogen bonds and extend throughout most of the receptor. We release with this work the code for C-Graphs and its graphical user interface.

Artificial intelligence in interdisciplinary life science and drug discovery research.

Instead of static entropy we assert that the Kolmogorov complexity of a static structure such as a solid is the proper measure of disorder (or chaoticity). A static structure in a surrounding perfectly-random universe acts as an interfering entity which introduces local disruption in randomness. This is modeled by a selection rule R which selects a subsequence of the random input sequence that hits the structure. Through the inequality that relates stochasticity and chaoticity of random binary sequences we maintain that Lin’s notion of stability corresponds to the stability of the frequency of 1s in the selected subsequence. This explains why more complex static structures are less stable. Lin’s third law is represented as the inevitable change that static structure undergo towards conforming to the universe’s perfect randomness.

Computational studies of organic systems are frequently limited to static pictures that closely align with textbook style presentations of reaction mechanisms and isomerization processes. Of course, in reality chemical systems are dynamic entities where a multitude of molecular conformations exists on incredibly complex potential energy surfaces (PES). Here, we borrow a computational technique originally conceived to be used in the context of biological simulations, together with empirical force fields, and apply it to organic chemical problems. Replica‐exchange molecular dynamics (REMD) permits thorough exploration of the PES. We combined REMD with density functional tight binding (DFTB), thereby establishing the level of accuracy necessary to analyze small molecular systems. Through the study of four prototypical problems: isomer identification, reaction mechanisms, temperature‐dependent rotational processes, and catalysis, we reveal new insights and chemistry that likely would be missed using static electronic structure computations. The REMD‐DFTB methodology at the heart of this study is powered by i‐PI, which efficiently handles the interface between the DFTB and REMD codes. © 2015 The Authors. Journal of Computational Chemistry Published by Wiley Periodicals, Inc.

Prediction of three-dimensional structures and structural flexibilities of wild-type and mutant cytochrome P450 1A2 using molecular dynamics simulations

Ion channels are proteins that form pores of nanoscopic dimensions in cell membranes. As a consequence of advance in protein crystallography we now know the three-dimensional structures of a number of ion channels. However, X-ray diffraction techniques yield an essentially static (time- and space-averaged) structure of an ion channel, in an environment often somewhat distantly related to that which the protein experiences when in a cell membrane. Thus, additional techniques are required to fully understand the relationship between channel structure and function. Potassium (K) channels (Yellen, 2002) provide an opportunity to explore the relationship between membrane protein structure, dynamics, and function. Furthermore, K channels are of considerable physiological and biomedical interest. They regulate K + ion flux across cell membranes. K channel regulation is accomplished by a conformational change that allows the protein to switch between two alternative (closed vs. open) conformations, a process known as gating. Gating is an inherently dynamic process that cannot be fully characterized by static structures alone. The elucidation of the structures of several K + channels (Mackinnon, 2003;

Scientific relevance. Currently, machine learning (ML) methods are widely used in the research and development of new pharmaceuticals. ML methods are particularly important for assessing the safety of pharmacologically active substances early in the research process because such safety assessments significantly reduce the risk of obtaining negative results in the future.Aim. This study aimed to review the main information and prediction resources that can be used for the assessment of the safety of pharmacologically active substances in silico.Discussion. Novel ML methods can identify the most likely molecular targets for a specific compound to interact with, based on structure–activity relationship analysis. In addition, ML methods can be used to search for potential therapeutic and adverse effects, as well as to study acute and specific toxicity, metabolism, and other pharmacodynamic, pharmacokinetic, and toxicological characteristics of investigational substances. Obtained at early stages of research, this information helps to prioritise areas for experimental testing of biological activity, as well as to identify compounds with a low probability of producing adverse and toxic effects. This review describes free online ML-based information and prediction resources for assessing the safety of pharmacologically active substances using their structural formulas. Special attention is paid to the Russian computational products presented on the Way2Drug platform (https://www.way2drug.com/dr/).Conclusions. Contemporary approaches to the assessment of pharmacologically active substances in silico based on structure–activity relationship analysis using ML methods provide information about various safety characteristics and allow developers to select the most promising candidates for further in-depth preclinical and clinical studies.

Background: It is important to be able to predict, for each individual patient, the likelihood of later metastatic occurrence, because the prediction can guide treatment plans tailored to a specific patient to prevent metastasis and to help avoid under-treatment or over-treatment. Deep neural network (DNN) learning, commonly referred to as deep learning, has become popular due to its success in image detection and prediction, but questions such as whether deep learning outperforms other machine learning methods when using non-image clinical data remain unanswered. Grid search has been introduced to deep learning hyperparameter tuning for the purpose of improving its prediction performance, but the effect of grid search on other machine learning methods are under-studied. In this research, we take the empirical approach to study the performance of deep learning and other machine learning methods when using non-image clinical data to predict the occurrence of breast cancer metastasis (BCM) 5, 10, or 15 years after the initial treatment. We developed prediction models using the deep feedforward neural network (DFNN) methods, as well as models using nine other machine learning methods, including naïve Bayes (NB), logistic regression (LR), support vector machine (SVM), LASSO, decision tree (DT), k-nearest neighbor (KNN), random forest (RF), AdaBoost (ADB), and XGBoost (XGB). We used grid search to tune hyperparameters for all methods. We then compared our feedforward deep learning models to the models trained using the nine other machine learning methods. Results: Based on the mean test AUC (Area under the ROC Curve) results, DFNN ranks 6th, 4th, and 3rd when predicting 5-year, 10-year, and 15-year BCM, respectively, out of 10 methods. The top performing methods in predicting 5-year BCM are XGB (1st), RF (2nd), and KNN (3rd). For predicting 10-year BCM, the top performers are XGB (1st), RF (2nd), and NB (3rd). Finally, for 15-year BCM, the top performers are SVM (1st), LR and LASSO (tied for 2nd), and DFNN (3rd). The ensemble methods RF and XGB outperform other methods when data are less balanced, while SVM, LR, LASSO, and DFNN outperform other methods when data are more balanced. Our statistical testing results show that at a significance level of 0.05, DFNN overall performs comparably to other machine learning methods when predicting 5-year, 10-year, and 15-year BCM. Conclusions: Our results show that deep learning with grid search overall performs at least as well as other machine learning methods when using non-image clinical data. It is interesting to note that some of the other machine learning methods, such as XGB, RF, and SVM, are very strong competitors of DFNN when incorporating grid search. It is also worth noting that the computation time required to do grid search with DFNN is much more than that required to do grid search with the other nine machine learning methods.

Unc-51 like kinase 1 (ULK1), a key regulator of autophagy initiation, is a novel target for anticancer drug design. In this work, we collected 846 ULK1 inhibitors with IC50 values from 30 references. Based on ECFP_4, MACCS fingerprints, and Mordred descriptors, we established a list of classification models by using Support Vector Machine (SVM), Random Forest (RF), extreme Gradient Boosting (XGBoost) and Deep Neural Networks (DNN). Additionally, several Fingerprint and Graph Neural Network (FP-GNN) models were also constructed using mixed molecular fingerprints and molecular graph. A total of 39 classification models were developed. Model_1D_1, an ECFP4-based DNN model, performed the best, achieving accuracies over 95% and Matthews correlation coefficient (MCC) over 0.9 on both validation and test sets. The applicability domain calculated by weighted Euclidean distance indicated that Model_1D_1 could reliably predict the activity for over 84% compounds in both training and test sets. We conducted structure-activity relationship (SAR) analysis through K-means and SHAP. The dataset’s molecular structures were classified into 7 subsets by K-means clustering. We identified three high-activity subsets sharing a common scaffold, 2-amino-4-(2-thienyl)-5-(trifluoromethyl)pyrimidine. SHAP analysis highlighted critical molecular fragments influencing activity, enhancing our understanding of model predictions and providing a theoretical basis for optimizing ULK1 inhibitors.

Four hydrates with d(001) = 8.4, 8.6, and 10 Å (two types) were synthesized by intercalating kaolinite with dimethylsulfoxide and treating the intercalated clay with fluoride ions. X-ray powder diffraction, infrared spectroscopy, differential scanning calorimetry, thermal gravimetric analysis, and kinetics of dehydration experiments have led to the identification of two types of interlayer water. One type of water (hole water) is situated in the ditrigonal holes of the silica tetrahedral surface; the second type (associated water) forms a discontinuous layer of mobile water. The 8.4-Å and 8.6-Å hydrates have only hole water, whereas the two synthetic 10-Å hydrates and halloysite(10Å) contain both hole and associated water. The hole water is probably hydrogen bonded to the basal oxygens of the silica tetrahedra or, in the 8-Å hydrates when fluorine exchanges for inner-surface hydroxyls, the water molecules may reorient and form stronger hydrogen bonds to the fluorine. Associated water forms water-water hydrogen bonds approximately equal in strength to liquid water but is less strongly bonded to the clay surfaces than hole water. At room temperature the hole and associated water in the 10-Å hydrates do not form an ice-like structure.

MOVES: Movable and moving LiDAR scene segmentation in label-free settings using static reconstruction

A structure is defined as a combination of organizational units and business processes by technological, organizational, hierarchical and other bonds. It is one of the management tools. Mining companies are characterized by a dynamic and static structure. A dynamic structure includes technological processes, which are determined by mining technology, the organization of mining works and a work system. The static structure consists of technological processes infrastructure and includes a group of organizational units connected by the specific standards and processes of their operation. The type of the structure, particularly in its static part, is defined considering the hierarchy and organizational units classification. Taking the hierarchy into account, we distinguish: linear, functional, linear-staff and matrix structures. Considering organizational units classification, we distinguish: functional, divisional, processing and mixed structures. Each structure can be described by the following features: specialization, hierarchization, centralization and formalization. The features mentioned above have a significant impact on the mining company activity. The dynamic structure of a mining company, considering basic and support technological processes, is described in the paper. Concerning static structures, special attention was paid to functional structures in the one-unit mining company, as well as on divisional structures of the multi-unit mining enterprise. Analyses of organizational structures of selected mining companies indicate that functional structures are suitable for small or one-unit mines. In the case of large mining companies, which through the dynamic development become the global operators of several mines/projects localized in different geographical regions or countries, their organizational structure should change from functional toward divisional.